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  <div>Poor management practices cost the global economy $8.8 trillion, or about 9% of world GDP! (Gallup) This course builds job-ready skills in business process modeling, analysis, and improvement that businesses desperately need; no prior experience required. </div><div> </div><div>Designed for professionals and aspiring managers, this course builds your practical knowledge of how Lean, Six Sigma, and systems thinking, can analyze and enhance how work gets done. </div><div> </div><div>Through activities such as podcasts and AI-enabled role-plays, you will model and map processes, identify inefficiencies, and apply proven methods to improve performance, communication, and collaboration across teams. Plus, through real-world scenarios and a final project, you’ll design and document a complete business process model and narrative.  </div><div> </div><div>By the end, you’ll be ready to contribute to process improvement initiatives and pursue certifications such as CompTIA Project+, ASQ CQIA, or AIGPE Process Mapping Specialist. </div><div> </div><div>Enroll today to strengthen your analytical skills and accelerate your career in business analysis and operations management. </div><h1>Course Syllabus</h1><div>This course comprises five modules, which are outlined below. You are encouraged to set aside 2–3 hours each week to complete all modules over the suggested five-week period. Maintaining a consistent study schedule will help you achieve your learning goals. <br /><br /><strong>Module 1: Process Modeling Fundamentals </strong></div><div><strong>Module 2: Business Process Mapping Techniques </strong></div><div><strong>Module 3: Business Process Improvement and AI <br />Module 4: Systems Thinking, Lean, and Six Sigma <br />Module 5: Final Project, Final Quiz and Course Wrap-Up </strong></div><div><br /></div><h1>Learning Objectives</h1><ul><li>Build job-ready skills in business process modeling, Lean, Six Sigma, and systems thinking to excel in business analysis and process management roles  </li><li>Develop and interpret process models and maps that represent current and desired states, enabling better communication and decision making  </li><li>Analyze processes to identify inefficiencies, bottlenecks, and improvement opportunities using mapping, narratives, and analytical frameworks  </li><li>Integrate systems thinking, Lean, and Six Sigma to design, implement, and sustain optimized processes with measurable impact </li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>This course is for people interested in business process modeling and  improvement, systems thinking, and applications of Lean, and Six Sigma</div>
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Business Process Modeling, Analysis, and Improvement

In this course, you will learn how to think and work like a business analyst or project manager, regardless of your current role or title. You will build practical skills in business process modeling, mapping, and improvement to help teams operate more efficiently and effectively. You will explore Lean, Six Sigma, systems thinking, and AI-supported analysis to identify inefficiencies, optimize workflows, and design sustainable improvements. 

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  <div>66% of Agile businesses use the Scrum framework (The Tech Report). Project-oriented professionals with strong Scrum Master skills are highly sought after! This course deepens your understanding of the essential aspects of being a Scrum Master and builds the skills employers are looking for. <br /><br /></div><div>What does a Scrum Master do? A Scrum Master facilitates and supports the Scrum team by ensuring adherence to Agile principles, removing obstacles, and fostering collaboration to help the team deliver value efficiently.  <br /><br /></div><div>During this course, you’ll be immersed in the world of Scrum. You’ll explore how Scrum compares with traditional project management methods, and you’ll gain a deeper appreciation of why Scrum is a game-changer in today’s dynamic business landscape.  <br /><br /></div><div>You’ll apply your Scrum Master skills to real-world scenarios through hands-on labs and projects, where you’ll navigate challenges, foster collaboration, and drive successful project outcomes. In addition, you’ll complete a final assessment that will develop your understanding of the criteria for certification as an Advanced Certified Scrum Master (A-CSM).  <br /><br /></div><div>If you’re looking to take your Scrum Master skills to a deeper level and stand out to employers, ENROLL TODAY and power up your resume with competencies that will catch their eye! <br /><br /></div><div> </div><div><strong>You will learn:</strong> </div><ul><li>Job-ready Scrum Master skills employers need in less than a month. </li><li>The Scrum Process flow and the various artifacts required at different phases of the process, as well as how to create Scrum personas.      </li><li>How to develop a working agreement to foster a collaborative environment among team members.   </li><li>How to run a retrospective for a given scenario and apply useful coaching techniques to resolve issues within a dysfunctional team.   </li><li>How to develop a product vision and goals while collaborating with the product owner to refine a backlog.   </li></ul><div> </div><div><br /><br /></div><h1>Course Syllabus</h1><div><strong>Welcome</strong> <br />• Video: Course Introduction <br />• Reading: General Information <br />• Reading: Learning Objectives and Syllabus <br />• Reading: Grading Scheme <br />• Reading: How to make the most of this course <br /> <br /><strong>Module 1: Scrum and Scrum Master Basics</strong> <br />• Reading: Module 1 Introduction and Learning Objectives <br />• Video: Scrum Process and Artifacts  <br />• Video: The Role of the Scrum Master <br />• Reading: Detailed Review of Scrum Artifacts <br />• Video: The Job Description of a Scrum Master <br />• Video: Expert Viewpoints: What Does a Scrum Master Do? <br />• Video: Comparing Scrum Values to Project Management Methodologies <br />• Reading: Key Players in the Scrum Process <br />• Video: Keys to Success: Skills, Attributes, and Personality Traits Essential to Scrum Master Success <br />• Video: Scrum Master Defined <br />• Reading: Key Leadership Traits Essential to Scrum Master Success <br />• Video: Introduction to Personas <br />• Lab: Defining the Scrum Process and Scrum Personas: Task 1 <br />• Lab: Defining the Scrum Process and Scrum Personas: Task 2 <br />• Reading: Module 1 Summary: Scrum and Scrum Master Basics <br />• Reading: Module 1 Glossary: Scrum and Scrum Master Basics <br />• Practice Quiz: Module 1: Scrum and Scrum Master Basics <br />• Graded Quiz: Module 1 Scrum and Scrum Master Basics <br />  <br /><strong>Module 2: Scrum Master as a Facilitator and Coach</strong> <br />• Reading: Module 2 Introduction and Learning Objectives <br />• Video: Managing Stakeholders <br />• Video: Engaging Stakeholders <br />• Reading: Analyzing Situational Facilitation Techniques to Maximize Collaboration and Various Coaching Techniques <br />• Video: Expert Viewpoints: Scrum Master as a Leader <br />• Video: Working Agreement  <br />• Lab: Develop a Working Agreement <br />• Reading: Acceptance Criteria and the Concept of “Done” <br />• Video: Managing the Daily Stand-up Meeting <br />• Video: Focusing the Team <br />• Video: Scrum Master as a Coach  <br />• Video: Expert Viewpoints: The Scrum Master as a Facilitator <br />• Video: The Sprint Retrospective <br />• Lab: Analyze a Scenario: Developing a Retrospective <br />• Reading: Module 2 Summary: Scrum Master as a Facilitator and a Coach <br />• Reading: Module 2 Glossary: Scrum Master as a Facilitator and a Coach <br />• Practice Quiz: Module 2: Scrum Master as a Facilitator and a Coach <br />• Graded Quiz: Module 2 Scrum Master as a Facilitator and a Coach <br />  <br /><strong>Module 3: Leadership: Scrum Master as a Servant Leader</strong> <br />• Reading: Module 3 Introduction and Learning Objectives <br />• Video: Servant Leadership <br />• Video: Define Qualities of a Self-Organized Team and Techniques to Achieve Team Maturity <br />• Video: Expert Viewpoints: Scrum Master as a Coach <br />• Video: Development Models to Improve Team Effectiveness and Continuous Improvement <br />• Lab: Getting a Dysfunctional Team Back on Track <br />• Video: Scrum Master and the Product Owner Collaboration <br />• Video: Expert Viewpoints: Servant Leadership <br />• Video: Develop a Product Vision and Determine Product Goals <br />• Lab: Review a Scenario. Create a Product Vision <br />• Video: Collaborating with the Product Owner to Refine the Backlog <br />• Video: Refining the Backlog <br />• Reading: Reading: Module 3 Summary: Leadership: Scrum Master as a Servant Leader <br />• Reading: Module 3 Glossary: Leadership: Scrum Master as a Servant Leader <br />• Practice Quiz: Module 3: Leadership: Scrum Master as a Servant Leader <br />• Graded Quiz: Module 3 Leadership: Scrum Master as a Servant Leader <br />  <br /><strong>Module 4: Navigating Complexity and Change</strong> <br />• Reading: Module 4 Introduction and Learning Objectives <br />• Video: Identifying Organizational Impediments <br />• Video: Dealing with Difficult Stakeholders <br />• Video: Expert Viewpoints: Product Owner Collaboration <br />• Video: Using the Force Field Analysis Tool <br />• Lab: Read a Scenario. Develop a Force Field Analysis <br />• Video: Conflict and the Team <br />• Video: Expert Viewpoints: Managing Complexity and Change <br />• Video: Recognizing Dependencies <br />• Reading: Evaluating and Responding to Team Interactions <br />• Video: Navigating Change <br />• Lab: Review a Scenario. Apply Change Models to Address the Change Factors <br />• Reading: Module 4 Summary: Navigating Complexity and Change <br />• Reading: Module 4 Glossary: Navigating Complexity and Change <br />• Practice Quiz: Module 4: Navigating Complexity and Change <br />• Graded Quiz: Module 4: Navigating Complexity and Change  <br />  <br /><strong>Module 5: Course Summary and Final Assessment</strong> <br />• Reading: Module 5 Introduction and Learning Objectives <br />• Reading: Review of Previous Modules and the Scrum Process <br />• Reading: The Path to Certification: PMI-ACP, DASM, and A-CSM Certification Exam <br />• Video: The Path to Certification <br />• Reading: Glossary of Terms <br />• Graded Quiz: Final Graded Exam <br />• Retake Exam <br />• Final Project: The Big Picture: Putting it All Together <br />• Reading: Final Project Scenario <br />• Lab: Health of the Project <br />• Lab: Recommendations <br />• Lab: Retrospective <br />• Final Project: Submission and Peer Review </div><div><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>To get the most out of this course, you should possess some basic project management skills and experience.  </div><div> </div>
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Scrum Master: The Path to Success

Get job-ready Scrum Master skills and valuable hands-on experience employers value in less than a month.  

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  <div>This Machine Learning Capstone is designed to showcase and solidify your expertise in Python-based machine learning. In this hands-on course, you’ll bring together everything you’ve learned in previous courses in the program and apply it to real-world problems using libraries such as Pandas, Scikit-learn, and TensorFlow/Keras. <br /><br /></div><div>Your main project will focus on building a course recommender system. You’ll work with course-related datasets, calculate cosine similarity, create similarity matrices, and experiment with multiple algorithms. By applying K-Nearest Neighbors (KNN), Principal Component Analysis (PCA), and non-negative matrix collaborative filtering, you will compare and contrast the performance of different machine learning approaches to recommendation systems. <br /><br /></div><div>Beyond recommendation systems, you'll also train a neural network to predict course ratings and build regression and classification models to enhance your predictive analytics skills. This project gives you the opportunity to demonstrate not just technical proficiency, but also critical thinking in evaluating and selecting the most effective models. </div><div><br />By the end of the course, you’ll have a portfolio-worthy project, practical experience with advanced machine learning techniques, and the confidence to apply your skills to real-world challenges. <br /><br /></div><h1>Learning Objectives</h1><div>• Apply advanced machine learning techniques using Python libraries such as Pandas, Scikit-learn, and TensorFlow/Keras </div><div>• Design and implement a real-world course recommender system using cosine similarity, KNN, PCA, and collaborative filtering methods </div><div>• Demonstrate proficiency in predictive analytics by building and evaluating regression, classification, and neural network models </div><div>• Showcase critical thinking and professional skills by comparing algorithms, selecting effective models, and delivering a portfolio-ready project <br /><br /></div><h1>Course Syllabus</h1><div>Welcome  </div><ul><li>Video: Introduction to Machine Learning Capstone </li><li>Ungraded Plugin: Capstone Overview </li></ul><div>Module 1: Machine Learning Capstone Overview </div><ul><li>Reading: Learning Objectives </li><li>Video: Introduction to Recommender Systems </li><li>Reading: Text Analysis </li><li>Reading: Stopwords and WordCloud </li><li>App Item: Exploratory Data Analysis on Online Course Enrollment Data </li><li>App Item: Extract Bag of Words (BoW) Features from Course Textual Content </li><li>Reading: Sparse and Dense Bag of Words (BOW) Vectors </li><li>Reading: Similarity Measures in Recommender Systems </li><li>App Item: Calculate Course Similarity using BoW Features </li><li>Practice Assignment: Checkpoints: Exploratory Data Analysis on Online Course Enrollment Data </li><li>Graded Assignment: Exploratory Data Analysis and Feature Engineering </li></ul><div>Module 2:  Unsupervised-Learning Based Recommender System </div><ul><li>Reading: Learning Objectives </li><li>Video: Content-based Recommender Systems </li><li>Reading: Evaluation Metrics of Recommender Systems </li><li>App Item: Content-based Course Recommender System using User Profile and Course Genres </li><li>Reading: Heatmaps </li><li>App Item: Content-based Course Recommender System using Course Similarities </li><li>App Item: Clustering-based Course Recommender System </li><li>Practice Assignment: Checkpoints:  Unsupervised-Learning Based Recommender System </li><li>Graded Assignment: Unsupervised-Learning Based Recommendation Systems </li></ul><div>Module 3: Supervised-Learning Based Recommender Systems </div><ul><li>Reading: Learning Objectives </li><li>Video: Collaborative Filtering-Based Recommender Systems </li><li>Reading: Exploring Surprise Library and KNN Model </li><li>App Item: Collaborative Filtering-based Recommender System using K Nearest Neighbor </li><li>App Item: Collaborative Filtering-based Recommender System using Non-negative Matrix Factorization </li><li>App Item: Course Rating Prediction using Neural Networks </li><li>App Item: Regression-based Rating Score Prediction Using Embedding Features <br />App Item: Classification-based Rating Mode Prediction using Embedding Features </li><li>Practice Assignment: Checkpoints: Supervised-Learning Based Recommender Systems </li><li>Graded Assignment: Supervised-Learning Based Recommendation Methods </li></ul><div>Module 4: Share and Present Your Recommender Systems </div><ul><li>Reading: Learning Objectives </li><li>Video: Elements Of A Successful Data Findings Report </li><li>Reading: Structure Of A Report </li><li>Video: Best Practices For Presenting Your Findings </li><li>(Optional) Hands-on Lab: Getting Started With PowerPoint For The Web </li><li>(Optional) Hands-on Lab: Basics of PowerPoint </li><li>(Optional) Hands-on Lab: Save your PowerPoint Presentation as PDF </li></ul><div>Module 5: Final Submission </div><ul><li>Reading: Learning Objectives </li><li>Final Submission Overview and Instructions </li><li>Exercise: Preparing Your Presentation (with provided slide template) </li><li>Peer Review: Submit Your Work and Review Your Peers </li><li>Reading: An Overview of the Streamlit Module </li><li>Ungraded Plugin: Introduction to Streamlit </li><li>Ungraded Plugin: Build a Course Recommender App with Streamlit </li><li>Reading: Congratulations and Next Steps </li><li>Reading: Thanks from the Course Team </li></ul><h1>General Information</h1><div>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari. <br /><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>Before taking this course, please ensure you have completed all of the other 5 courses in the edx Machine Learning Professional Certificate. </div><div>To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics.</div>
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Machine Learning: Capstone Project

Demonstrate your machine learning expertise in this IBM Capstone course. Apply Pandas, Scikit-learn, TensorFlow/Keras, and build a real-world course recommender system. Showcase your ML skills through this comprehensive hands-on project.

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  <div>Discover how generative AI can help you create engaging, personalized learning content and improve your productivity so you can enhance student outcomes. <br /><br /></div><div>During this course, you’ll explore gen AI methodologies and best practices for implementing AI in education. You’ll look at how it can be used to create content, enhance personalized learning, special education, and adaptive learning. You’ll get hands-on with popular gen AI tools, including IBM watsonx, ChatGPT, Claude, and Khanmigo in education. Plus, you’ll investigate the ethical implementation of gen AI, considering privacy protection, bias mitigation, and the creation of inclusive learning environments.   <br /><br /></div><div>Throughout, you’ll hear from industry experts, who share insights on the future of gen AI in education. You’ll build your gen AI expertise in hands-on labs and interactive activities. Plus, you’ll complete a final project where you’ll apply all that you’ve learned to a practical, real-world scenario.  <br /><br /></div><div>Whether you’re an educator looking to revolutionize your classroom practices or simply want to streamline ad<br />ministrative tasks, this course will give you the gen AI skills you need to excel. Enroll today!<br /><br /><strong>Prerequisites</strong> </div><div> </div><div>This course is designed for educators who already have an educational or teaching background and want to enhance their teaching capabilities using gen AI. You should have a fundamental understanding of gen AI and instructional design. The following courses in the <strong>Generative AI for Educators</strong> specialization can help you become familiar with gen AI basics and prompt engineering techniques: </div><div> </div><ul><li><a href="https://www.coursera.org/learn/generative-ai-introduction-and-applications?specialization=generative-ai-for-everyone">Course 1: Generative AI: Introduction and Applications</a> </li><li><a href="https://www.coursera.org/learn/generative-ai-prompt-engineering-for-everyone?specialization=generative-ai-for-everyone">Course 2: Generative AI: Prompt Engineering Basics</a> </li></ul><div> </div><div><strong>Objectives</strong> </div><div>After completing this course, you will be able to: </div><div> </div><ul><li>Identify appropriate gen AI tools for education, considering pedagogy, integration ease, and cost, to address specific teaching needs </li><li>Align the content with learners’ abilities, preferences, and engagement goals to create personalized learning content </li><li>Leverage gen AI tools to design lesson plans and assessments to encourage dynamic and interactive learning </li><li>Apply ethical AI practices by safeguarding data privacy, mitigating bias, and promoting an inclusive learning environment </li></ul><div> </div><div> </div><div><strong>Course Outline</strong> </div><div> </div><div>This course is divided into three modules. You can complete each module at your own pace, but it’s recommended to set a goal of completing at least one module per week.   </div><div> </div><div><strong>Module 1: Gen AI Tools and Personalized Learning </strong> <br /> </div><div>In this module, you will explore the landscape of educational gen AI tools and select them based on pedagogical needs, integration capabilities, and cost considerations. You will also learn to evaluate the effectiveness of AI tools for different teaching requirements and identify principles for AI-enhanced personalized learning and advanced content customization techniques. Finally, you will be able to leverage AI-powered tools for content creation and implement strategies for differentiating instruction using AI-powered resources.  </div><div> </div><div> </div><div> </div><div><strong>Module 2: AI Integration and Performance Assessment</strong> <br /> </div><div>This module introduces you to how to integrate AI tools seamlessly into teaching practices and leverage them for learners’ evaluation. You will gain insight into designing AI-enhanced lesson planning aligning them with learning objectives. Additionally, you will learn to create assessment strategies using gen AI tools to gauge learners’ mastery of concepts and analyze AI-generated performance data. Finally, you will be able to leverage AI-powered feedback mechanisms to provide timely, data-driven insights that support learners’ growth and continuous improvement.  </div><div> </div><div> <br /><strong>Module 3: Ethical Implementation and Applied Project</strong> </div><div> </div><div>This module helps address ethical considerations while implementing gen AI in education, recognize bias, and develop its mitigation strategies. You will also learn to design data privacy protection strategies for AI integration. </div><div> </div><div>This module includes storyline interactivity based on the ethical guidelines while implementing gen AI in education.  </div><div> </div><div>The module includes a final project based on the concepts covered in the course. In this module, you will attempt a graded quiz to test and reinforce your understanding of concepts. The module also includes a glossary to enhance comprehension of generative AI-related terms.   <br /> <br /> </div><div> </div><div><strong>Tools/Software</strong> </div><div>In this course, you will explore no-cost versions of various gen AI tools and platforms, including: </div><div> </div><ul><li>Microsoft’s Copilot </li><li>OpenAI’s ChatGPT – A versatile AI language model for creating lesson plans, answering learners’ queries, and personalized tutoring  </li><li>Anthropic Claude – An advanced conversational AI model for generating insightful educational content and assisting in curriculum design </li><li>Khanmigo Tools for Teachers – A suite of AI-powered tools designed to assist teachers in lesson planning, curriculum alignment, and classroom management </li><li>TeachFlow – A tool for streamlining teaching workflows, providing AI-driven assistance for lesson planning and classroom management </li><li>Synthesia – A tool for creating engaging animated videos  </li></ul><div> </div><div>These tools are accessible via modern web browsers such as Chrome, Firefox, and Edge. Learners are not required to install these tools on their devices. </div><div> </div><div><strong>Tips for Success</strong> </div><ul><li>Stay organized and keep track of teaching strategies and learning requirements </li><li>Consistently practice using the gen AI tools and technologies covered in the course </li><li>Experiment with the examples provided to enhance your understanding and application of AI in education </li></ul><div> </div><div>Congratulations on taking this step to elevate your teaching career with gen AI! Enjoy your learning journey. </div>
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Generative AI: Skills & Tools for Educators

This course is for education professionals, including teachers, professors, instructional designers, education administrators, EdTech professionals, corporate trainers, education policymakers, online course creators, and consultants who want to advance their careers by leveraging the power of generative AI. 

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  <div>This course explores two of the most dynamic and in-demand areas of machine learning: Deep Learning and Reinforcement Learning. You’ll begin by diving into Deep Learning, a subset of machine learning that powers many modern AI systems—from image and speech recognition to natural language processing.  </div><div><br />The course introduces the foundational theory behind neural networks, explaining how they are structured, how they learn, and why they are effective for handling complex, high-dimensional data. You’ll gain hands-on experience building and training neural networks and explore modern deep learning architectures that are widely used in industry. <br /><br /></div><div>You’ll then move on to Reinforcement Learning (RL), a rapidly growing field in AI that focuses on decision-making and learning through interaction with an environment. Reinforcement Learning has practical applications in research, across robotics, autonomous systems, game-playing AI, and beyond. You’ll learn the key concepts of RL, including agents, environments, actions, rewards, and policies, and how these elements work together in the learning process. </div><div><br />By the end of this course, you will have developed a strong understanding of how Deep Learning and Reinforcement Learning differ from traditional supervised and unsupervised learning approaches. You’ll also be able to design, build, and interpret basic deep learning models, as well as grasp the foundational principles that drive reinforcement learning strategies. <br /><br /></div><h1>Learning Objectives</h1><div>• Demonstrate understanding of deep learning by explaining how neural networks are structured, trained, and applied to complex data </div><div>• Apply technical skills to design, build, and train basic deep learning models using modern architectures </div><div>• Show working knowledge of reinforcement learning by identifying agents, environments, actions, rewards, and policies in AI systems </div><div>• Differentiate between supervised, unsupervised, deep, and reinforcement learning approaches to highlight their unique applications <br /><br /></div><h1>Course Syllabus</h1><div>Course Introduction </div><ul><li>Video: Course Introduction </li></ul><div>Module 1: Introduction to Neural Networks </div><ul><li>Reading: Learning Objectives </li><li>Video: Introduction to Neural Networks </li><li>Video: Basics of Neurons </li><li>Video: Neural Networks with Sigmoid Function </li><li>Video: Neuron in Action </li><li>Video: Neural Networks with SKlearn </li><li>App Item: Neural Networks with Sklearn </li><li>Video: Forward Propagation </li><li>Video: Matrix Representation of Forward Propagation </li><li>Video: Main Types of Deep Neural Network </li><li>App Item: Introduction to Neural Networks Demo (Activity) </li><li>Video: (Optional) Introduction to Neural Networks Notebook - Part 1 </li><li>Video: (Optional) Introduction to Neural Networks Notebook - Part 2 </li><li>Practice Assignment: Introduction to Neural Networks </li><li>Video: Gradient Descent Basics </li><li>Video: Compare Different Gradient Descent Methods </li><li>App Item: Gradient Descent Demo (Activity) </li><li>Video: (Optional) Gradient Descent Notebook - Part 1 </li><li>Video: (Optional) Gradient Descent Notebook - Part 2 </li><li>Video: (Optional) Gradient Descent Notebook - Part 3 </li><li>Practice Assignment: Optimization and Gradient Descent </li><li>Reading: Summary/Review </li><li>Module 1 Graded Quiz </li></ul><div>Module 2: Back Propagation Training and Keras </div><ul><li>Reading: Learning Objectives </li><li>Video: How to Train a Neural Network </li><li>Video: Backpropagation </li><li>App Item: Backpropagation Demo (Activity) </li><li>Video: (Optional) Backpropagation Notebook - Part 1 </li><li>Video: (Optional) Backpropagation Notebook - Part 2 </li><li>Video: The Sigmoid Activation Function </li><li>Video: Other Popular Activation Functions </li><li>Video: (Optional) Backpropagation Notebook - Part 3 </li><li>Practice Assignment: Practice: Back Propagation, Activation Functions </li><li>Video: Popular Deep Learning Library </li><li>Video: A Typical Keras Workflow </li><li>Video: Implementing an Example Neural Network in Keras </li><li>App Item: Keras Demo (Activity) </li><li>Video: (Optional) Keras Notebook - Part 1 </li><li>Video: (Optional) Keras Notebook - Part 2 </li><li>Video: (Optional) Keras Notebook - Part 3  </li><li>App Item: Regression with Keras </li><li>App Item: (Optional) Loading Images with Keras </li><li>Practice Assignment: Keras Library </li><li>Reading: Summary/Review </li><li>Module 2 Graded Quiz </li></ul><div>Module 3: Neural Network Optimizers </div><ul><li>Reading: Learning Objectives </li><li>Video: Optimizers and Momentum </li><li>Video: Regularization Techniques for Deep Learning </li><li>Video: Popular Optimizers </li><li>Video: Details of Training Neural Networks </li><li>Reading: Learning Rate Scheduler Reading </li><li>Video: Data Shuffling  </li><li>App Item: Optimizers </li><li>App Item: Grid Search with Keras </li><li>Video: Transforms </li><li>Practice Assignment: Optimizers and Data Shuffling </li><li>Reading: Summary/Review </li><li>Module 3 Graded Quiz </li></ul><div>Module 4: Convolutional Neural Networks </div><ul><li>Reading: Learning Objectives </li><li>Video: Categorical Cross Entropy </li><li>App Item: Categorical Cross Entropy </li><li>Video: Introduction to Convolutional Neural Networks (CNN) </li><li>Video: Images Dataset </li><li>Video: Kernels </li><li>Video: Convolution for Color Images </li><li>App Item: Images Convolution </li><li>Video: Convolutional Settings - Padding and Stride </li><li>App Item: Padding, Pooling, and Stride </li><li>Video: Convolutional Settings - Depth and Pooling </li><li>App Item: Channels and Flattening </li><li>App Item: Training the Network </li><li>App Item: Convolutional Neural Networks Demo (Activity) </li><li>Video: (Optional) Demo CNN Notebook - Part 1 </li><li>Video: (Optional) Demo CNN Notebook - Part 2 </li><li>Practice Assignment: Convolutional Neural Networks </li><li>Reading: Summary/Review </li><li>Module 4 Graded Quiz  </li></ul><div>Module 5: Transfer Learning </div><ul><li>Reading: Learning Objectives </li><li>Video: Introduction to Transfer Learning </li><li>Video: Transfer Learning and Fine Tuning </li><li>App Item: Transfer Learning Demo (Activity) </li><li>Video: (Optional) Transfer Learning Notebook </li><li>Practice Assignment: Transfer Learning </li><li>Video: Convolutional Neural Network Architectures – LeNet </li><li>Video: Convolutional Neural Network Architectures – AlexNet </li><li>Video: VGG </li><li>Video: Convolutional Neural Network Architectures – Inception </li><li>Video: Convolutional Neural Network Architectures – ResNet </li><li>App Item: Types of Model APIs in Keras </li><li>App Item: Transfer Learning Examples with Existing Architectures </li><li>Practice Assignment: Convolutional Neural Network Architectures </li><li>Reading: Regularization </li><li>App Item: Regularization Techniques </li><li>Practice Assignment: Regularization </li><li>Reading: Summary/Review </li><li>Module 5 Graded Quiz </li></ul><div>Module 6: Recurrent Neural Networks and Long-Short Term Memory Networks </div><ul><li>Reading: Learning Objectives </li><li>Video: Recurrent Neural Networks (RNNs) </li><li>Video: State and  Recurrent Neural Networks </li><li>App Item: (Optional) Introduction to Sequential Data </li><li>App Item: Existing Recurrent Neural Networks </li><li>Video: Details Recurrent Neural Networks </li><li>App Item: Word Embeddings </li><li>App Item: Recurrent Neural Networks Demo (Activity) </li><li>Video: (Optional) Recurrent Neural Networks Notebook - Part 1 </li><li>Video: (Optional) Recurrent Neural Networks Notebook - Part 2 </li><li>Practice Assignment: Recurrent Neural Networks </li><li>Video: Long-Short Term Memory (LSTM) Networks </li><li>Video: LSTM Explanation </li><li>Video: Gated Recurrent Unit </li><li>Video: Gated Recurrent Unit Details </li><li>App Item: LSTM and GRU Demo (Activity) </li><li>Practice Assignment: LSTM and GRU </li><li>Reading: Summary/Review </li><li>Module 6: Graded Quiz </li></ul><div>Module 7: Autoencoders </div><ul><li>Reading: Learning Objectives </li><li>Video: Introduction to Autoencoders </li><li>Video: Autoencoders </li><li>Ungraded Plugin: Transposed Convolution Reading </li><li>App Item: Autoencoders </li><li>Practice Assignment: Autoencoders </li><li>Autoencoders Demo (Activity) </li><li>Video: (Optional) Autoencoders Notebook - Part 1 </li><li>Video: (Optional) Autoencoders Notebook - Part 2 </li><li>Video: (Optional) Autoencoders Notebook - Part 3 </li><li>Video: (Optional) Autoencoders Notebook - Part 4 </li><li>Video: (Optional) Autoencoders Notebook - Part 5 </li><li>Reading: Summary/Review </li><li>Module 7 Graded Quiz  </li></ul><div>Module 8: Generative Models and Applications of Deep Learning </div><ul><li>Reading: Learning Objectives </li><li>Video: What is a Variational Autoencoder </li><li>Video: How Variational Autoencoders Work </li><li>App Item: Variational Autoencoder </li><li>Practice Assignment: Variational Autoencoders </li><li>Video: Introduction to GANs </li><li>Video: How GANS Work </li><li>Video: Issues with Training GANS </li><li>App Item: GANS Lab 1 </li><li>App Item: GANS Lab 2 </li><li>Video: Additional Topics in Deep Learning </li><li>App Item: GPU with Keras </li><li>Video: Model Agnostic Explainable AI </li><li>Practice Assignment: Generative Adversarial Networks </li><li>Reading: Summary/Review </li><li>Module 8 Graded Quiz </li></ul><div>Module 9: Reinforcement Learning </div><ul><li>Reading: Learning Objectives </li><li>Video: Reinforcement Learning (RL) </li><li>App Item: Reinforcement Learning Demo (Activity) </li><li>Video: (Optional) Reinforcement Learning Notebook - Part 1 </li><li>Video: (Optional) Reinforcement Learning Notebook - Part 2 </li><li>Video: (Optional) Reinforcement Learning Notebook - Part 3 </li><li>Video: (Optional) Reinforcement Learning Notebook - Part 4 </li><li>Practice Assignment: Reinforcement Learning </li><li>Reading: Summary/Review </li><li>Module 9 Graded Quiz </li><li>Peer Review: Final Project </li><li>Reading: Thanks from the Course Team </li></ul><div><br /></div><h1>General Information</h1><div>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari. <br /><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics. </div>
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Machine Learning: Deep & Reinforcement Learning

Get started in Reinforcement and Deep Learning! Explore the theory and practice behind neural networks, build deep learning models, and study modern architectures used in today’s AI applications.

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  <div>Gain a detailed introduction to unsupervised learning, one of the core branches of machine learning focused on uncovering patterns and insights in data without labeled outcomes. Learn how to work with datasets that lack a target variable by applying powerful techniques such as clustering and dimensionality reduction. These methods are essential for identifying structure within the data, discovering hidden groupings, and simplifying complex datasets while retaining meaningful information. <br /><br /></div><div>Throughout the course, you’ll explore commonly used algorithms like k-means, hierarchical clustering, DBSCAN, and principal component analysis (PCA), as well as learn how to choose the most appropriate method based on the nature of your data. You’ll gain an understanding of the curse of dimensionality and how it affects the performance of clustering algorithms when working with high-dimensional data.</div><div><br />The hands-on component emphasizes real-world problem solving using best practices in unsupervised learning. You’ll gain practical experience implementing clustering and dimensionality reduction techniques, interpreting the results, and applying appropriate metrics to evaluate the quality of your clusters. </div><div><br />By the end of the course, you’ll be equipped to identify when unsupervised learning is appropriate, apply different algorithms effectively, and extract meaningful insights from unlabeled datasets. </div><div><br /></div><h1>Learning Objectives</h1><div>• Define the core concepts of unsupervised learning and identify when to apply clustering and dimensionality reduction to unlabeled datasets. </div><div>• Implement algorithms such as k-means, hierarchical clustering, DBSCAN, and PCA using tools like NumPy to uncover hidden patterns and simplify complex data. </div><div>• Analyze clustering results, interpret dimensionality reduction outputs, and evaluate model performance while addressing challenges such as the curse of dimensionality. </div><div>• Select and justify the most appropriate unsupervised learning technique for real-world problems to extract meaningful insights from data. <br /><br /></div><h1>Course Syllabus</h1><div>Course Introduction </div><ul><li>Video: Course Introduction </li></ul><div>Module 1: Introduction to Unsupervised Learning and K Means </div><ul><li>Reading: Learning Objectives </li><li>Video: Introduction to Unsupervised Learning: Overview </li><li>Video: Introduction to Unsupervised Learning: Use Cases of Clustering </li><li>Video: Introduction to Clustering </li><li>Ungraded Practice Assignment: Introduction to Unsupervised Learning </li><li>Video: K-Means </li><li>Video: K-Means Initialization </li><li>Video: Selecting the Right Number of Clusters in K-Means </li><li>Video: Elbow method and Applying K-means </li><li>K Means Demo (Activity) </li><li>Video: (Optional) K Means Notebook - Part 1 </li><li>Video: (Optional) K Means Notebook - Part 2 </li><li>Video: (Optional) K Means Notebook - Part 3 </li><li>Practice Lab: K Means Clustering Lab </li><li>Reading: Mixture of Gaussians </li><li>Practice Lab: Mixture of Gaussians Lab </li><li>Ungraded Practice Assignment: K Means Clustering </li><li>Reading: Summary </li><li>Graded: Module 1 Quiz </li></ul><div>Module 2: Distance Metrics &amp; Computational Hurdles </div><ul><li>Reading: Learning Objectives </li><li>Video: Distance Metrics: Euclidean and Manhattan Distance </li><li>Video: Distance Metrics: Cosine and Jaccard Distance </li><li>Demo lab: Curse of Dimensionality </li><li>Video: (Optional) Curse of Dimensionality Notebook - Part 1 </li><li>Video: (Optional) Curse of Dimensionality Notebook - Part 2 </li><li>Video: (Optional) Curse of Dimensionality Notebook - Part 3 </li><li>Video: (Optional) Curse of Dimensionality Notebook - Part 4 </li><li>Practice Lab: Distance Metrics Lab </li><li>Ungraded Practice Assignment: Distance Metrics </li><li>Reading: Summary </li><li>Graded: Module 2 Quiz </li></ul><div>Module 3: Selecting a Clustering Algorithm </div><ul><li>Reading: Learning Objectives </li><li>Video: Hierarchical Agglomerative Clustering </li><li>Video: Hierarchical Agglomerative Clustering: Hierarchical Linkage Types </li><li>Video: Applying Hierarchical Agglomerative Clustering </li><li>Video: DBSCAN </li><li>Video: Visualizing DBSCAN </li><li>Practice lab: DBSCAN Clustering </li><li>Video: Mean Shift </li><li>Practice lab: Mean Shift Clustering </li><li>Ungraded Practice Assignment: Clustering Algorithms </li><li>Video: Comparing Algorithms </li><li>Clustering Demo (Activity) </li><li>Video: Clustering Notebook - Part 1 </li><li>Video: Clustering Notebook - Part 2 </li><li>Video: (Optional) Clustering Notebook - Part 3 </li><li>Video: (Optional) Clustering Notebook - Part 4 </li><li>Ungraded Practice Assignment: Comparing Clustering Algorithms </li><li>Reading: Summary </li><li>Graded: Module 3 Quiz </li></ul><div>Module 4: Dimensionality Reduction </div><ul><li>Reading: Learning Objectives </li><li>Video: Dimensionality Reduction:  Overview </li><li>Video: Dimensionality Reduction: Principal Component Analysis </li><li>App Item: (Optional) Matrix Review </li><li>Demo lab: Dimensionality Reduction (Part 1) </li><li>Video: (Optional) Dimensionality Reduction Notebook - Part 1 </li><li>Video: (Optional) Dimensionality Reduction Notebook - Part 2 </li><li>Practice lab: Principal Component Analysis </li><li>App Item: Singular Value Decomposition </li><li>Video: Dimensionality Reduction Imaging Example </li><li>Ungraded Practice Assignment: Dimensionality Reduction  </li><li>Reading: Summary </li><li>Graded: Module 4 Quiz </li></ul><div>Module 5: Nonlinear and Distance-Based Dimensionality Reduction </div><ul><li>Reading: Learning Objectives </li><li>Video: Kernel  Principal Component Analysis and Multidimensional Scaling </li><li>Demo lab: Dimensionality Reduction (Part 2) </li><li>Video: (Optional) Dimensionality Reduction Notebook - Part 3 </li><li>Practice lab: Kernel PCA </li><li>Practice lab: Multidimensional Scaling </li><li>Ungraded Practice Assignment: Kernel PCA and MDS </li><li>Reading: Summary </li><li>Graded: Module 5 Quiz  </li></ul><div>Module 6: Matrix Factorization </div><ul><li>Reading: Learning Objectives </li><li>Video: Non Negative Matrix Factorization </li><li>Demo lab: Non-Negative Matrix Factorization </li><li>App Item: (Optional) TF-IDF Supplemental. </li><li>Video: (Optional) Non Negative Matrix Factorization Notebook - Part 1 </li><li>Video: (Optional) Non Negative Matrix Factorization Notebook - Part 2 </li><li>Practice lab: Non-Negative Matrix Factorization </li><li>Ungraded Practice Assignment: Non Negative Matrix Factorization </li><li>Reading: Summary </li><li>Graded: Module 6 Quiz </li></ul><div>Module 7: Final Project </div><ul><li>Peer Review: Course Final Project </li><li>Reading: Thanks from the Course Team </li></ul><h1>General Information</h1><div>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari. <br /><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics. </div>
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Machine Learning: Unsupervised Models

This course introduces key unsupervised machine learning techniques and explains how to choose the best algorithm for your data. Learn clustering and dimensionality reduction to find insights in unlabeled data sets. 

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  <div>This course provides an introduction to classification, a fundamental technique in supervised machine learning used to predict categorical outcomes. Learn how to build, train, and evaluate predictive models using methods such as logistic regression, decision trees, and powerful ensemble techniques like random forests and gradient boosting.  </div><div> </div><div>You’ll gain hands-on experience with essential machine learning practices, including properly splitting data into training and testing sets to avoid overfitting and using techniques like oversampling and undersampling to handle unbalanced datasets. This ensures your models are both accurate and robust when applied to real-world data. </div><div> </div><div>A key focus of the course is on model evaluation using a range of error metrics, helping you compare performance and choose the best model for your data. By the end of the course, you will understand when to use classification versus other supervised learning methods, how to implement and interpret different classification algorithms, and how to use best practices to ensure your models are effective and generalizable. </div><div> </div><div>This course is ideal for aspiring machine learning engineers and data scientists looking to apply classification techniques in practical business scenarios. Whether you’re aiming to predict customer churn, detect fraud, or categorize products, this course will equip you with the skills needed to solve real-world classification problems. </div><div><br /></div><div>To succeed in the course, you should be comfortable with Python programming and have a foundational understanding of data cleaning, exploratory data analysis, calculus, linear algebra, probability, and statistics. </div><div><br /></div><h1>Learning Objectives</h1><div>• Build and evaluate classification models using logistic regression, decision trees, random forests, and gradient boosting. </div><div>• Apply best practices in model training and testing to improve generalization and prevent overfitting. </div><div>• Handle unbalanced datasets with techniques such as oversampling and undersampling. </div><div>• Compare classification models using performance metrics to select the most effective solution for real-world problems. <br /><br /></div><h1>Course Syllabus</h1><div>Course Introduction </div><ul><li>Reading: About this course </li><li>Video: Welcome </li><li>Reading: Optional: Download data assets </li></ul><div>Module 1: Logistic Regression </div><ul><li>Reading: Learning Objectives </li><li>Video: Introduction: What is Classification? </li><li>Video: Introduction to Logistic Regression </li><li>Video: Classification with Logistic Regression </li><li>Video: Logistic Regression with Multi-Classes </li><li>Video: Implementing Logistic Regression Models </li><li>Video: Confusion Matrix, Accuracy, Specificity, Precision, and Recall </li><li>Video: Classification Error Metrics: ROC and Precision-Recall Curves </li><li>Video: Implementing the Calculation of ROC and Precision-Recall Curves </li><li>Practice Assignment: Logistic Regression </li><li>Demo Lab: Logistic Regression </li><li>Reading: [Optional] Download Assets for Demo Lab: Logistic Regression </li><li>Video: [Optional] Logistic Regression Lab - Part 1 </li><li>Video: [Optional] Logistic Regression Lab - Part 2 </li><li>Video: [Optional] Logistic Regression Lab - Part 3 </li><li>Practice Lab: Logistic Regression </li><li>Practice Assignment: Logistic Regression Labs </li><li>Reading: Summary/Review </li><li>Module 1 Graded Quiz Logisitic Regression </li></ul><div>Module 2: K Nearest Neighbors </div><ul><li>Reading: Learning Objectives </li><li>Video: K Nearest Neighbors for Classification </li><li>Video: K Nearest Neighbors Decision Boundary </li><li>Video: K Nearest Neighbors Distance Measurement </li><li>Video: K Nearest Neighbors Pros and Cons </li><li>Video: K Nearest Neighbors with Feature Scaling </li><li>Practice Assignment: K Nearest Neighbors </li><li>Demo Lab: K Nearest Neighbors </li><li>Video: [Optional] K Nearest Neighbors Notebook - Part 1 </li><li>Video: [Optional] K Nearest Neighbors Notebook - Part 2 </li><li>Video: [Optional] K Nearest Neighbors Notebook - Part 3 </li><li>Practice Lab: K Nearest Neighbors </li><li>Practice Assignment: K Nearest Neighbors Labs </li><li>Reading: Summary/Review </li><li>Module 2 Graded Quiz – KNN </li></ul><div>Module 3: Support Vector Machines </div><ul><li>Reading: Learning Objectives </li><li>Video: Introduction to Support Vector Machines </li><li>Video: Classification with Support Vector Machines </li><li>Video: The Support Vector Machines Cost Function </li><li>Video: Regularization in Support Vector Machines </li><li>Practice Assignment: Support Vector Machines </li><li>Video: Introduction to Support Vector Machines Gaussian Kernels </li><li>Video: Support Vector Machines Gaussian Kernels - Part 1 </li><li>Video: Support Vector Machines Gaussian Kernels - Part 2 </li><li>Video: Support Vector Machines Workflow </li><li>Video: Implementing Support Vector Machines Kernal Models </li><li>Practice Assignment: Support Vector Machines Kernels </li><li>Demo Lab: Support Vector Machines </li><li>Video: [Optional] Support Vector Machines Notebook - Part 1 </li><li>Video: [Optional] Support Vector Machines Notebook - Part 2 </li><li>Video: [Optional] Support Vector Machines Notebook - Part 3 </li><li>Practice Lab: Support Vector Machines </li><li>Practice Assignment: Support Vector Machines Labs </li><li>Reading: Summary/Review </li><li>Module 3 Graded Quiz: Support Vector Machines  </li></ul><div>Module 4: Decision Trees </div><ul><li>Reading: Learning Objectives </li><li>Video: Overview of Classifiers </li><li>Video: Introduction to Decision Trees </li><li>Video: Building a Decision Tree </li><li>Video: Entropy-based Splitting </li><li>Video: Other Decision Tree Splitting Criteria </li><li>Video: Pros and Cons of Decision Trees </li><li>Practice Assignment: Decision Trees </li><li>Demo Lab: Decision Trees </li><li>Reading: [Optional] Download Assets for Demo Lab: Decision Trees </li><li>Video: [Optional] Decision Trees Notebook - Part 1 </li><li>Video: [Optional] Decision Trees Notebook - Part 2 </li><li>Video: [Optional] Decision Trees Notebook - Part 3 </li><li>Practice Lab: Decision Trees </li><li>Practice Assignment: Decision Trees Labs </li><li>Reading: Summary/Review </li><li>Module 4 Graded Quiz: Decision Trees </li></ul><div>Module 5: Ensemble Models </div><ul><li>Reading: Learning Objectives </li><li>Video: Ensemble Based Methods and Bagging - Part 1 </li><li>Video: Ensemble Based Methods and Bagging - Part 2 </li><li>Video: Ensemble Based Methods and Bagging - Part 3 </li><li>Practice Assignment: Bagging </li><li>Video: Random Forest </li><li>Practice Lab: Random Forest </li><li>Practice Assignment: Random Forest </li><li>Demo Lab: Bagging </li><li>Reading: [Optional] Download Assets for Demo Lab: Bagging </li><li>Video: [Optional] Bagging Notebook - Part 1 </li><li>Video: [Optional] Bagging Notebook - Part 2 </li><li>Video: [Optional] Bagging Notebook - Part 3 </li><li>Practice Lab: Bagging </li><li>Practice Assignment: Practice Lab: Bagging </li><li>Video: Review of Bagging </li><li>Video: Overview of Boosting </li><li>Video: Adaboost and Gradient Boosting Overview </li><li>Video: Adaboost and Gradient Boosting Syntax </li><li>Video: Stacking </li><li>Practice Assignment: Boosting and Stacking </li><li>Demo Lab: Boosting and Stacking </li><li>Reading: [Optional] Download Assets for Demo Lab: Boosting and Stacking </li><li>Video: [Optional] Boosting Notebook - Part 1 </li><li>Video: [Optional] Boosting Notebook - Part 2 </li><li>Video: [Optional] Boosting Notebook - Part 3 </li><li>Practice Lab: Ada Boost </li><li>Practice Lab: Stacking For Classification with Python </li><li>Practice Lab: (Optional) Gradient Boosting </li><li>Practice Assignment: Boosting and Stacking Labs </li><li>Reading: Summary/Review </li><li>Module 5 Graded Quiz </li></ul><div> </div><div>Module 6: Modeling Unbalanced Classes </div><ul><li>Reading: Learning Objectives </li><li>Video: Model Interpretability </li><li>Video: Examples of Self-Interpretable and Non-Self-Interpretable Models </li><li>Video: Model-Agnostic Explanations </li><li>Video: Surrogate Models </li><li>Practice Lab: Model Interpretability </li><li>Practice Assignment:  Model interpretability </li><li>Video: Introduction to Unbalanced Classes </li><li>Video: Upsampling and Downsampling </li><li>Video: Modeling Approaches: Weighting and Stratified Sampling </li><li>Video: Modeling Approaches: Random and Synthetic Oversampling </li><li>Video: Modeling Approaches: Nearing Neighbor Methods </li><li>Video: Modeling Approaches: Blagging </li><li>Practice Lab: Modeling Imbalanced Classes </li><li>Practice Assignment: Modeling Unbalanced Classes </li><li>Reading: Summary/Review </li><li>Module 6 Graded Quiz </li><li>Peer Assignment: Course Final Project </li><li>Reading: Thanks from the Course Team </li></ul><div><br /></div><h1>General Information</h1><div>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari. <br /><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics. </div>
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Machine Learning: Classification

This course covers key supervised machine learning (ML) and classification techniques, including logistic regression, decision trees, ensemble methods, and handling unbalanced datasets. Build and evaluate classification models using real-world data.

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  <div>Explain to a potential learner why this course is important/interesting and what they can gain from taking your course.<br /><br /></div><h1>Course Syllabus</h1><div><strong>Module 1: Generative AI Foundations for Requirements Engineering <br /></strong><br /></div><div>Explore how generative AI supports requirement gathering, stakeholder interviews, and specification drafting. Learn to design prompts, simulate interview scenarios, and convert raw business inputs into structured requirement artifacts such as user stories, use cases, and acceptance criteria. <br /><br /></div><div><strong>Module 2: Process Modeling and Documentation with Generative AI <br /></strong><br /></div><div>Learn to auto-generate BPMN diagrams, process flows, and system architecture visuals from text inputs. Practice converting informal notes into formal documentation, critiquing workflows, and creating business rules and visual reporting assets using AI. <br /><br /></div><div><strong>Module 3: Analysis, Testing, and Compliance with Generative AI <br /></strong><br /></div><div>Enhance your analysis and communication skills using AI-assisted review, executive summaries, stakeholder mapping, and multilingual communication. Generate test cases, simulate deployment environments, and identify performance and compliance considerations using Generative AI. </div><div><strong><br />Module 4: Final Project, Final Quiz, and Course Wrap-Up <br /></strong><br /></div><div><br /></div><h1>General Information</h1><div>This is your place to add any additional information about your course. Feel free to mention the assessment style, a recommended timeline of events, or any other important information that a learner should know.<br /><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>Let your learners know what technology and skills they'll need prior to starting this guided project. Remember that the IBM Skills Network Labs environment comes with many things pre-installed (e.g. Docker) to save them the hassle of setting everything up. Also note that this platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari.</div>
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Generative AI: Advancing Systems Analysis & Architecture

Are you a Systems Analyst, IT Systems Architect, Software Architect, or a Solutions Architect and keen to use generative AI (GenAI) to enhance how you analyze and design systems, define solution requirements, and share technical insights?  In this course, you’ll learn how to craft prompts and apply AI-driven requirement extraction to convert unstructured inputs, such as stakeholder interviews, emails, and process notes, into structured documentation, including user stories, use cases, system requirements, and acceptance criteria.

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  <div><strong><br />Course outline<br /></strong><br /></div><div><strong>Module 1: Using Generative AI for SEO<br /></strong><br /></div><div>A hands-on sprint that integrates generative AI into the traditional SEO workflow. You’ll learn AI-driven keyword discovery, create topic clusters and content briefs, and optimize blog drafts for intent, readability, and internal linking. You will also generate meta titles, descriptions, multilingual variants, and JSON-LD schemas (article, FAQ, product), and validate the outputs for rich-result eligibility. Short videos explain the “why”; labs provide prompt patterns and reusable templates; quizzes reinforce guardrails so speed never sacrifices accuracy.<br /><br /></div><div><strong>Key learning activities:<br /></strong><br /></div><ul><li><br />AI-driven keyword research and clustering</li><li><br />Prompt-based content brief creation</li><li><br />AI-assisted blog editing and internal linking improvements</li><li><br />Generating and validating meta tags and JSON-LD schema</li><li><br />Practice quiz and module summary materials</li></ul><div><strong><br />Module 2: Final Project, Final Quiz, and Course Wrap-Up<br /></strong><br /></div><div><br />Apply what you’ve learned in a practical final project: Craft an SEO-friendly blog for a researched keyword and perform on-page optimization using the provided workflow and prompts. Complete a graded final quiz to demonstrate mastery, review the course glossary, and conclude with a wrap-up that consolidates practical templates and outlines next steps.<br /><br /></div><div><strong><br />Key learning activities:<br /></strong><br /></div><ul><li><br />Final project: SEO blog creation and optimization</li><li><br />Final exam and glossary review</li><li><br />Course wrap-up and suggested next learning pathways</li></ul><div><strong><br />Tools and software<br /></strong><br /></div><div><br />Work inside the <strong>Generative AI Classroom</strong> authoring environment to test prompts and models. Labs also reference free/trial versions of tools such as <strong>ChatGPT</strong> and <strong>Gemini</strong> for hands-on practice. Paid subscriptions are <strong>not required</strong>.<br /><br /></div>
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Gen AI for SEO: A Hands-On Playbook

Generative AI (GenAI) is now a key power tool in an SEO professional’s toolkit. In this beginner-friendly course, you’ll learn how to leverage its power using tools like ChatGPT, Gemini, Claude, Semrush, and Keyword Insights AI to build AI-driven SEO workflows that boost speed, accuracy, and creativity. You’ll learn how to use GenAI for SEO to conduct AI-driven keyword research, create topic clusters, and build content briefs that align with your target audience’s search intent. 

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  <div>Ready to put your ethical hacking skills to the test and power your cybersecurity career forward? <br /><br />This hands-on course is ideal for aspiring cybersecurity analysts, ethical hackers, and IT professionals who want to be able to demonstrate their real-world skills in offensive and defensive security to employers. <br /><br />During this course, you’ll start on the Red Team, using industry-standard tools such as Kali Linux, Nmap, and Metasploit to conduct reconnaissance, exploitation, privilege escalation, and persistence. Then, you’ll shift to the Blue Team, where you’ll practice log analysis, detect Indicators of Compromise, and respond to attacks using OpenVAS and the MITRE ATT&amp;CK framework.<br /><br />You’ll also explore the growing role of generative AI in cybersecurity, applying AI-assisted reconnaissance, exploit generation, and anomaly detection to strengthen your strategies.<br /><br />Each lab will challenge you to prove your mastery, culminating in a professional-grade incident response report.<br /><br />If you’re keen to showcase your ethical hacking prowess and cybersecurity skills through a portfolio-worthy capstone, enroll today!</div>
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Ethical Hacking Capstone Project: Breach, Response, AI

Master advanced ethical hacking techniques by exploring cybersecurity assessment, threat modeling, and vulnerability management. Gain expertise in vulnerability scanning and the MITRE ATT&CK framework to enhance your ability to detect and prevent intrusions. Learn to navigate complex breach scenarios using AI-driven strategies for effective response. This capstone project empowers learners to safeguard systems with cutting-edge skills.

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  <div>Leverage the power of generative AI (GenAI) to transform your UI UX design capabilities!<br /><br />This course builds the hands-on skills designers and creative professionals need to integrate AI into every stage of the user experience workflow—from ideation and research to prototyping and conversational design.<br /><br />You’ll explore leading tools such as UXPilot, Miro, Visily, Uizard and Figma and learn how you can use AI to streamline tasks such as generating personas and journey maps, creating UX microcopy, and simulating chatbot interactions—while ensuring design human-centered, inclusive, and accessible.<br /><br />In hands-on labs, you’ll build practical skills in AI-assisted wireframing, usability testing, and conversational UX design. Plus, you’ll complete a final project, where you’ll design an AI-powered experience, apply prompt engineering, and refine your solution through feedback loops.<br /><br />Within just 4 weeks, you’ll be ready to embrace AI as a collaborative design partner, and to work faster, smarter, and more innovatively.<br /><br />If you’re looking to power your UI/UX career forward and lead the next wave of AI-enhanced digital experiences, enroll today!</div>
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Generative AI: The Future of UX UI Design

A basic understanding of UI, UX, and generative AI is recommended. Familiarity with design tools, prompts, and user research methods is advantageous.

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  <div><strong>Imagine you're writing code</strong>, whether it's your first Python script or a complex application. You need AI assistance—for debugging, optimization, or architectural advice—but with so many models available, how do you choose the right one?<br /><br /><strong>AI coding assistants</strong> like <strong>Claude 3.7 Sonnet</strong> and <strong>IBM Granite 3.2 8B Instruct</strong> promise to help, but their abilities vary. <em>Does reasoning mode actually improve suggestions? Is one model better for beginners versus complex projects?</em> In this hands-on lab, you'll test these models across <strong>four practical coding tasks</strong>—from basic syntax checks to system design—to see which AI truly elevates your development process. With the <strong>Generative AI Classroom</strong>, compare models side-by-side—<strong>no setup, no fees, no guesswork</strong>. Just log in, run experiments, and discover which AI best supports your coding journey.<br /><br /></div><h1><strong>Project Overview</strong></h1><div>This lab benchmarks <strong>Claude 3.7 Sonnet vs. IBM Granite 3.2 8B Instruct</strong> across coding tasks of increasing complexity: <br />1️⃣ <strong>Basic Code Review</strong> (syntax errors, style fixes) <br />2️⃣ <strong>Algorithm Analysis</strong> (time/space complexity optimizations)<br /> 3️⃣ <strong>System Design</strong> (authentication flows, architecture patterns)<br /> 4️⃣ <strong>End-to-End Feedback</strong> (readability, maintainability, scalability)<br /><br />You’ll evaluate them on three key metrics: <br />✅ <strong>Speed</strong> – Response times for quick iterations <br />✅ <strong>Cost</strong> – Value per query at scale<br /> ✅ <strong>Accuracy</strong> – Error detection and suggestion quality<br /><br /></div><h1><strong>What You’ll Learn</strong></h1><div>By completing this lab, you will:</div><ul><li>Compare <strong>reasoning vs. standard modes</strong> for coding tasks</li><li>Identify which model excels at <strong>beginner support</strong> vs. <strong>expert-level design</strong></li><li>Learn <strong>prompting techniques</strong> to get better coding help</li><li>Gain <strong>hands-on experience</strong> with AI-assisted development</li></ul><h1><strong>Who Should Do This Lab?</strong></h1><div>Perfect for anyone who codes—no matter your level:</div><ul><li><strong>Beginners</strong> seeking AI tutoring for fundamentals</li><li><strong>Intermediate devs</strong> optimizing algorithms</li><li><strong>Senior engineers</strong> evaluating AI for architecture reviews</li><li><strong>Educators</strong> comparing AI teaching tools</li></ul><div><strong>No advanced skills needed</strong>—start with simple code and progress at your pace.</div><div><br /></div><h1><strong>What You Need</strong></h1><div>✅ <strong>A browser</strong> (Chrome/Firefox/Safari etc.) <br />✅ <strong>Basic coding awareness</strong> (any language)<br /> ✅ <strong>Curiosity</strong>—test prompts and draw conclusions!<br /><br /><strong>Zero installations</strong>—everything runs in your browser. By the end, you’ll know whether <strong>Claude’s detailed analysis</strong> or <strong>Granite’s quick feedback</strong> better matches your needs—and how to leverage both effectively.</div>
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AI Code Review Showdown: Anthropic's Claude vs IBM's Granite

Compare Anthropic's Claude 3.7 Sonnet and IBM's Granite 3.2 8B Instruct models for Python code review tasks in both reasoning and non-reasoning modes. This lab evaluates how these hybrid reasoning models perform when analyzing syntax errors, algorithms, authentication systems, and architecture. Discover which model delivers better accuracy, speed, and cost-efficiency for different coding scenarios. Learn when reasoning mode provides advantages over non-reasoning mode and identify optimal use cases for each approach based on comprehensive performance metrics.

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  <div>The Machine Learning Capstone allows you to showcase and strengthen your Python-based machine learning skills. In this hands-on course, you’ll integrate concepts from previous courses and apply them to real-world problems using Pandas, Scikit-learn, and TensorFlow/Keras. </div><div><br />Your primary project involves building a course recommender system. You’ll work with course datasets, calculate cosine similarity, generate similarity matrices, and experiment with multiple algorithms. Using K-Nearest Neighbors (KNN), Principal Component Analysis (PCA), and non-negative matrix collaborative filtering, you’ll compare and evaluate the performance of different recommendation approaches. </div><div><br />In addition, you’ll train a neural network to predict course ratings and develop regression and classification models to enhance your predictive analytics expertise. This project emphasizes both technical skills and critical thinking in model evaluation and selection. <br /><br /></div><div>By course completion, you’ll have a portfolio-ready project, hands-on experience with advanced machine learning techniques, and the confidence to apply your skills to real-world challenges. <br /><br /></div><h1>Learning Objectives</h1><div>• Apply advanced machine learning techniques using Python libraries like Pandas, Scikit-learn, and TensorFlow/Keras. </div><div>• Design and implement a real-world course recommender system using cosine similarity, KNN, PCA, and collaborative filtering. </div><div>• Build and evaluate regression, classification, and neural network models to demonstrate predictive analytics skills. </div><div>• Highlight critical thinking and professional expertise by comparing algorithms, selecting optimal models, and completing a portfolio-ready project. <br /><br /></div><h1>Course Syllabus</h1><div>Welcome  </div><ul><li>Video: Introduction to Machine Learning Capstone </li><li>Ungraded Plugin: Capstone Overview </li></ul><div> </div><div>Module 1: Machine Learning Capstone Overview </div><ul><li>Reading: Learning Objectives </li><li>Video: Introduction to Recommender Systems </li><li>Reading: Text Analysis </li><li>Reading: Stopwords and WordCloud </li><li>App Item: Exploratory Data Analysis on Online Course Enrollment Data </li><li>App Item: Extract Bag of Words (BoW) Features from Course Textual Content </li><li>Reading: Sparse and Dense Bag of Words (BOW) Vectors </li><li>Reading: Similarity Measures in Recommender Systems </li><li>App Item: Calculate Course Similarity using BoW Features </li><li>Practice Assignment: Checkpoints: Exploratory Data Analysis on Online Course Enrollment Data </li><li>Graded Assignment: Exploratory Data Analysis and Feature Engineering </li></ul><div> </div><div>Module 2:  Unsupervised-Learning Based Recommender System </div><ul><li>Reading: Learning Objectives </li><li>Video: Content-based Recommender Systems </li><li>Reading: Evaluation Metrics of Recommender Systems </li><li>App Item: Content-based Course Recommender System using User Profile and Course Genres </li><li>Reading: Heatmaps </li><li>App Item: Content-based Course Recommender System using Course Similarities </li><li>App Item: Clustering-based Course Recommender System </li><li>Practice Assignment: Checkpoints:  Unsupervised-Learning Based Recommender System </li><li>Graded Assignment: Unsupervised-Learning Based Recommendation Systems </li></ul><div> </div><div>Module 3: Supervised-Learning Based Recommender Systems </div><ul><li>Reading: Learning Objectives </li><li>Video: Collaborative Filtering-Based Recommender Systems </li><li>Reading: Exploring Surprise Library and KNN Model </li><li>App Item: Collaborative Filtering-based Recommender System using K Nearest Neighbor </li><li>App Item: Collaborative Filtering-based Recommender System using Non-negative Matrix Factorization </li><li>App Item: Course Rating Prediction using Neural Networks </li><li>App Item: Regression-based Rating Score Prediction Using Embedding Features <br />App Item: Classification-based Rating Mode Prediction using Embedding Features </li><li>Practice Assignment: Checkpoints: Supervised-Learning Based Recommender Systems </li><li>Graded Assignment: Supervised-Learning Based Recommendation Methods </li></ul><div> </div><div>Module 4: Share and Present Your Recommender Systems </div><ul><li>Reading: Learning Objectives </li><li>Video: Elements Of A Successful Data Findings Report </li><li>Reading: Structure Of A Report </li><li>Video: Best Practices For Presenting Your Findings </li><li>(Optional) Hands-on Lab: Getting Started With PowerPoint For The Web </li><li>(Optional) Hands-on Lab: Basics of PowerPoint </li><li>(Optional) Hands-on Lab: Save your PowerPoint Presentation as PDF </li></ul><div> </div><div>Module 5: Final Submission </div><ul><li>Reading: Learning Objectives </li><li>Final Submission Overview and Instructions </li><li>Exercise: Preparing Your Presentation (with provided slide template) </li><li>Peer Review: Submit Your Work and Review Your Peers </li><li>Reading: An Overview of the Streamlit Module </li><li>Ungraded Plugin: Introduction to Streamlit </li><li>Ungraded Plugin: Build a Course Recommender App with Streamlit </li><li>Reading: Congratulations and Next Steps </li><li>Reading: Thanks from the Course Team </li></ul><div><br /></div><h1>General Information</h1><div>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari. </div><div><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>The following skills are required to be successful with this course:  </div><ul><li>We recommend completing all previous courses in the IBM Machine Learning Professional Certificate before starting this capstone. </li></ul>
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Showcase your machine learning skills in this IBM Capstone course. Use Pandas, Scikit-learn, and TensorFlow/Keras to build a real-world course recommender system through a hands-on project.

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  <div>This course dives into two of the most sought-after areas of machine learning: Deep Learning and Reinforcement Learning. Start with Deep Learning, the AI backbone behind image and speech recognition, natural language processing, and other advanced applications. </div><div><br />You’ll learn the theory behind neural networks, including their structure, training methods, and why they excel at handling complex, high-dimensional data. Gain hands-on experience building and training networks and explore modern deep learning architectures widely used in industry. </div><div><br />Next, explore Reinforcement Learning (RL), an AI field focused on decision-making through interaction with environments. Learn key RL concepts: agents, environments, actions, rewards, and policies and see how they combine to enable learning in robotics, autonomous systems, game AI, and more. </div><div><br />By the end of the course, you’ll understand how Deep Learning and RL differ from traditional supervised and unsupervised learning, be able to design and implement basic deep learning models, and grasp the foundational principles of reinforcement learning strategies. <br /><br /></div><h1>Learning Objectives</h1><div>• Explain deep learning concepts, including neural network structure, training processes, and applications to complex datasets. </div><div>• Design, build, and train foundational deep learning models using modern architectures.</div><div>• Apply reinforcement learning principles by identifying agents, environments, actions, rewards, and policies in AI systems. </div><div>• Distinguish between supervised, unsupervised, deep, and reinforcement learning, highlighting their specific use cases and applications. <br /><br /></div><h1>Course Syllabus</h1><div>Course Introduction </div><ul><li>Video: Course Introduction </li></ul><div>Module 1: Introduction to Neural Networks </div><ul><li>Reading: Learning Objectives </li><li>Video: Introduction to Neural Networks </li><li>Video: Basics of Neurons </li><li>Video: Neural Networks with Sigmoid Function </li><li>Video: Neuron in Action </li><li>Video: Neural Networks with SKlearn </li><li>App Item: Neural Networks with Sklearn </li><li>Video: Forward Propagation </li><li>Video: Matrix Representation of Forward Propagation </li><li>Video: Main Types of Deep Neural Network </li><li>App Item: Introduction to Neural Networks Demo (Activity) </li><li>Video: (Optional) Introduction to Neural Networks Notebook - Part 1 </li><li>Video: (Optional) Introduction to Neural Networks Notebook - Part 2 </li><li>Practice Assignment: Introduction to Neural Networks </li><li>Video: Gradient Descent Basics </li><li>Video: Compare Different Gradient Descent Methods </li><li>App Item: Gradient Descent Demo (Activity) </li><li>Video: (Optional) Gradient Descent Notebook - Part 1 </li><li>Video: (Optional) Gradient Descent Notebook - Part 2 </li><li>Video: (Optional) Gradient Descent Notebook - Part 3 </li><li>Practice Assignment: Optimization and Gradient Descent </li><li>Reading: Summary/Review </li><li>Module 1 Graded Quiz </li></ul><div>Module 2: Back Propagation Training and Keras </div><ul><li>Reading: Learning Objectives </li><li>Video: How to Train a Neural Network </li><li>Video: Backpropagation </li><li>App Item: Backpropagation Demo (Activity) </li><li>Video: (Optional) Backpropagation Notebook - Part 1 </li><li>Video: (Optional) Backpropagation Notebook - Part 2 </li><li>Video: The Sigmoid Activation Function </li><li>Video: Other Popular Activation Functions </li><li>Video: (Optional) Backpropagation Notebook - Part 3 </li><li>Practice Assignment: Practice: Back Propagation, Activation Functions </li><li>Video: Popular Deep Learning Library </li><li>Video: A Typical Keras Workflow </li><li>Video: Implementing an Example Neural Network in Keras </li><li>App Item: Keras Demo (Activity) </li><li>Video: (Optional) Keras Notebook - Part 1 </li><li>Video: (Optional) Keras Notebook - Part 2 </li><li>Video: (Optional) Keras Notebook - Part 3  </li><li>App Item: Regression with Keras </li><li>App Item: (Optional) Loading Images with Keras </li><li>Practice Assignment: Keras Library </li><li>Reading: Summary/Review </li><li>Module 2 Graded Quiz  </li></ul><div>Module 3: Neural Network Optimizers </div><ul><li>Reading: Learning Objectives </li><li>Video: Optimizers and Momentum </li><li>Video: Regularization Techniques for Deep Learning </li><li>Video: Popular Optimizers </li><li>Video: Details of Training Neural Networks </li><li>Reading: Learning Rate Scheduler Reading </li><li>Video: Data Shuffling  </li><li>App Item: Optimizers </li><li>App Item: Grid Search with Keras </li><li>Video: Transforms </li><li>Practice Assignment: Optimizers and Data Shuffling </li><li>Reading: Summary/Review </li><li>Module 3 Graded Quiz </li></ul><div>Module 4: Convolutional Neural Networks </div><ul><li>Reading: Learning Objectives </li><li>Video: Categorical Cross Entropy </li><li>App Item: Categorical Cross Entropy </li><li>Video: Introduction to Convolutional Neural Networks (CNN) </li><li>Video: Images Dataset </li><li>Video: Kernels </li><li>Video: Convolution for Color Images </li><li>App Item: Images Convolution </li><li>Video: Convolutional Settings - Padding and Stride </li><li>App Item: Padding, Pooling, and Stride </li><li>Video: Convolutional Settings - Depth and Pooling </li><li>App Item: Channels and Flattening </li><li>App Item: Training the Network </li><li>App Item: Convolutional Neural Networks Demo (Activity) </li><li>Video: (Optional) Demo CNN Notebook - Part 1 </li><li>Video: (Optional) Demo CNN Notebook - Part 2 </li><li>Practice Assignment: Convolutional Neural Networks </li><li>Reading: Summary/Review </li><li>Module 4 Graded Quiz </li></ul><div>Module 5: Transfer Learning </div><ul><li>Reading: Learning Objectives </li><li>Video: Introduction to Transfer Learning </li><li>Video: Transfer Learning and Fine Tuning </li><li>App Item: Transfer Learning Demo (Activity) </li><li>Video: (Optional) Transfer Learning Notebook </li><li>Practice Assignment: Transfer Learning </li><li>Video: Convolutional Neural Network Architectures – LeNet </li><li>Video: Convolutional Neural Network Architectures – AlexNet </li><li>Video: VGG </li><li>Video: Convolutional Neural Network Architectures – Inception </li><li>Video: Convolutional Neural Network Architectures – ResNet </li><li>App Item: Types of Model APIs in Keras </li><li>App Item: Transfer Learning Examples with Existing Architectures </li><li>Practice Assignment: Convolutional Neural Network Architectures </li><li>Reading: Regularization </li><li>App Item: Regularization Techniques </li><li>Practice Assignment: Regularization </li><li>Reading: Summary/Review </li><li>Module 5 Graded Quiz </li></ul><div>Module 6: Recurrent Neural Networks and Long-Short Term Memory Networks </div><ul><li>Reading: Learning Objectives </li><li>Video: Recurrent Neural Networks (RNNs) </li><li>Video: State and  Recurrent Neural Networks </li><li>App Item: (Optional) Introduction to Sequential Data </li><li>App Item: Existing Recurrent Neural Networks </li><li>Video: Details Recurrent Neural Networks </li><li>App Item: Word Embeddings </li><li>App Item: Recurrent Neural Networks Demo (Activity) </li><li>Video: (Optional) Recurrent Neural Networks Notebook - Part 1 </li><li>Video: (Optional) Recurrent Neural Networks Notebook - Part 2 </li><li>Practice Assignment: Recurrent Neural Networks </li><li>Video: Long-Short Term Memory (LSTM) Networks </li><li>Video: LSTM Explanation </li><li>Video: Gated Recurrent Unit </li><li>Video: Gated Recurrent Unit Details </li><li>App Item: LSTM and GRU Demo (Activity) </li><li>Practice Assignment: LSTM and GRU </li><li>Reading: Summary/Review </li><li>Module 6: Graded Quiz </li></ul><div>Module 7: Autoencoders </div><ul><li>Reading: Learning Objectives </li><li>Video: Introduction to Autoencoders </li><li>Video: Autoencoders </li><li>Ungraded Plugin: Transposed Convolution Reading </li><li>App Item: Autoencoders </li><li>Practice Assignment: Autoencoders </li><li>Autoencoders Demo (Activity) </li><li>Video: (Optional) Autoencoders Notebook - Part 1 </li><li>Video: (Optional) Autoencoders Notebook - Part 2 </li><li>Video: (Optional) Autoencoders Notebook - Part 3 </li><li>Video: (Optional) Autoencoders Notebook - Part 4 </li><li>Video: (Optional) Autoencoders Notebook - Part 5 </li><li>Reading: Summary/Review </li><li>Module 7 Graded Quiz  </li></ul><div>Module 8: Generative Models and Applications of Deep Learning </div><ul><li>Reading: Learning Objectives </li><li>Video: What is a Variational Autoencoder </li><li>Video: How Variational Autoencoders Work </li><li>App Item: Variational Autoencoder </li><li>Practice Assignment: Variational Autoencoders </li><li>Video: Introduction to GANs </li><li>Video: How GANS Work </li><li>Video: Issues with Training GANS </li><li>App Item: GANS Lab 1 </li><li>App Item: GANS Lab 2 </li><li>Video: Additional Topics in Deep Learning </li><li>App Item: GPU with Keras </li><li>Video: Model Agnostic Explainable AI </li><li>Practice Assignment: Generative Adversarial Networks </li><li>Reading: Summary/Review </li><li>Module 8 Graded Quiz </li></ul><div>Module 9: Reinforcement Learning </div><ul><li>Reading: Learning Objectives </li><li>Video: Reinforcement Learning (RL) </li><li>App Item: Reinforcement Learning Demo (Activity) </li><li>Video: (Optional) Reinforcement Learning Notebook - Part 1 </li><li>Video: (Optional) Reinforcement Learning Notebook - Part 2 </li><li>Video: (Optional) Reinforcement Learning Notebook - Part 3 </li><li>Video: (Optional) Reinforcement Learning Notebook - Part 4 </li><li>Practice Assignment: Reinforcement Learning </li><li>Reading: Summary/Review </li><li>Module 9 Graded Quiz </li><li>Peer Review: Final Project </li><li>Reading: Thanks from the Course Team </li></ul><div><br /></div><h1>General Information</h1><div>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari. <br /><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics. </div>
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Start your journey in Reinforcement and Deep Learning! Learn neural network theory, build deep learning models, and explore modern AI architectures used in real-world applications. 

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  <div>This course introduces classification, a core technique in supervised machine learning used to predict categorical outcomes. You’ll learn to build, train, and evaluate classification models using logistic regression, decision trees, and advanced ensemble methods like random forests and gradient boosting. <br /><br /></div><div>Gain practical experience applying essential ML techniques, including splitting data for training and testing to prevent overfitting and handling unbalanced datasets through oversampling and undersampling. These practices help ensure your models perform reliably on real-world data. <br /><br /></div><div>You’ll also learn how to evaluate models using key performance metrics to compare accuracy and select the most effective approach. By the end of the course, you’ll understand how to implement and interpret various classification algorithms, distinguish classification from other supervised learning types, and apply best practices for building robust, generalizable models. <br /><br /></div><div>Designed for aspiring data scientists and machine learning engineers, this course provides the practical foundation to tackle business challenges like customer churn prediction, fraud detection, and product categorization using classification methods. <br /><br /></div><h1>Learning Objectives</h1><div>• Develop and assess classification models using logistic regression, decision trees, random forests, and gradient boosting. <br />• Apply effective training and testing strategies to enhance generalization and minimize overfitting. <br />• Manage unbalanced datasets with methods like oversampling and undersampling. <br />• Evaluate and compare models using performance metrics to identify the best solution for real-world applications. <br /><br /></div><h1>Course Syllabus</h1><div>Course Introduction </div><ul><li>Reading: About this course </li><li>Video: Welcome </li><li>Reading: Optional: Download data assets </li></ul><div>Module 1: Logistic Regression </div><ul><li>Reading: Learning Objectives </li><li>Video: Introduction: What is Classification? </li><li>Video: Introduction to Logistic Regression </li><li>Video: Classification with Logistic Regression </li><li>Video: Logistic Regression with Multi-Classes </li><li>Video: Implementing Logistic Regression Models </li><li>Video: Confusion Matrix, Accuracy, Specificity, Precision, and Recall </li><li>Video: Classification Error Metrics: ROC and Precision-Recall Curves </li><li>Video: Implementing the Calculation of ROC and Precision-Recall Curves </li><li>Practice Assignment: Logistic Regression </li><li>Demo Lab: Logistic Regression </li><li>Reading: [Optional] Download Assets for Demo Lab: Logistic Regression </li><li>Video: [Optional] Logistic Regression Lab - Part 1 </li><li>Video: [Optional] Logistic Regression Lab - Part 2 </li><li>Video: [Optional] Logistic Regression Lab - Part 3 </li><li>Practice Lab: Logistic Regression </li><li>Practice Assignment: Logistic Regression Labs </li><li>Reading: Summary/Review </li><li>Module 1 Graded Quiz Logisitic Regression </li></ul><div> </div><div>Module 2: K Nearest Neighbors </div><ul><li>Reading: Learning Objectives </li><li>Video: K Nearest Neighbors for Classification </li><li>Video: K Nearest Neighbors Decision Boundary </li><li>Video: K Nearest Neighbors Distance Measurement </li><li>Video: K Nearest Neighbors Pros and Cons </li><li>Video: K Nearest Neighbors with Feature Scaling </li><li>Practice Assignment: K Nearest Neighbors </li><li>Demo Lab: K Nearest Neighbors </li><li>Video: [Optional] K Nearest Neighbors Notebook - Part 1 </li><li>Video: [Optional] K Nearest Neighbors Notebook - Part 2 </li><li>Video: [Optional] K Nearest Neighbors Notebook - Part 3 </li><li>Practice Lab: K Nearest Neighbors </li><li>Practice Assignment: K Nearest Neighbors Labs </li><li>Reading: Summary/Review </li><li>Module 2 Graded Quiz – KNN </li></ul><div> </div><div>Module 3: Support Vector Machines </div><ul><li>Reading: Learning Objectives </li><li>Video: Introduction to Support Vector Machines </li><li>Video: Classification with Support Vector Machines </li><li>Video: The Support Vector Machines Cost Function </li><li>Video: Regularization in Support Vector Machines </li><li>Practice Assignment: Support Vector Machines </li><li>Video: Introduction to Support Vector Machines Gaussian Kernels </li><li>Video: Support Vector Machines Gaussian Kernels - Part 1 </li><li>Video: Support Vector Machines Gaussian Kernels - Part 2 </li><li>Video: Support Vector Machines Workflow </li><li>Video: Implementing Support Vector Machines Kernal Models </li><li>Practice Assignment: Support Vector Machines Kernels </li><li>Demo Lab: Support Vector Machines </li><li>Video: [Optional] Support Vector Machines Notebook - Part 1 </li><li>Video: [Optional] Support Vector Machines Notebook - Part 2 </li><li>Video: [Optional] Support Vector Machines Notebook - Part 3 </li><li>Practice Lab: Support Vector Machines </li><li>Practice Assignment: Support Vector Machines Labs </li><li>Reading: Summary/Review </li><li>Module 3 Graded Quiz: Support Vector Machines </li></ul><div> </div><div>Module 4: Decision Trees </div><ul><li>Reading: Learning Objectives </li><li>Video: Overview of Classifiers </li><li>Video: Introduction to Decision Trees </li><li>Video: Building a Decision Tree </li><li>Video: Entropy-based Splitting </li><li>Video: Other Decision Tree Splitting Criteria </li><li>Video: Pros and Cons of Decision Trees </li><li>Practice Assignment: Decision Trees </li><li>Demo Lab: Decision Trees </li><li>Reading: [Optional] Download Assets for Demo Lab: Decision Trees </li><li>Video: [Optional] Decision Trees Notebook - Part 1 </li><li>Video: [Optional] Decision Trees Notebook - Part 2 </li><li>Video: [Optional] Decision Trees Notebook - Part 3 </li><li>Practice Lab: Decision Trees </li><li>Practice Assignment: Decision Trees Labs </li><li>Reading: Summary/Review </li><li>Module 4 Graded Quiz: Decision Trees </li></ul><div> </div><div>Module 5: Ensemble Models </div><ul><li>Reading: Learning Objectives </li><li>Video: Ensemble Based Methods and Bagging - Part 1 </li><li>Video: Ensemble Based Methods and Bagging - Part 2 </li><li>Video: Ensemble Based Methods and Bagging - Part 3 </li><li>Practice Assignment: Bagging </li><li>Video: Random Forest </li><li>Practice Lab: Random Forest </li><li>Practice Assignment: Random Forest </li><li>Demo Lab: Bagging </li><li>Reading: [Optional] Download Assets for Demo Lab: Bagging </li><li>Video: [Optional] Bagging Notebook - Part 1 </li><li>Video: [Optional] Bagging Notebook - Part 2 </li><li>Video: [Optional] Bagging Notebook - Part 3 </li><li>Practice Lab: Bagging </li><li>Practice Assignment: Practice Lab: Bagging </li><li>Video: Review of Bagging </li><li>Video: Overview of Boosting </li><li>Video: Adaboost and Gradient Boosting Overview </li><li>Video: Adaboost and Gradient Boosting Syntax </li><li>Video: Stacking </li><li>Practice Assignment: Boosting and Stacking </li><li>Demo Lab: Boosting and Stacking </li><li>Reading: [Optional] Download Assets for Demo Lab: Boosting and Stacking </li><li>Video: [Optional] Boosting Notebook - Part 1 </li><li>Video: [Optional] Boosting Notebook - Part 2 </li><li>Video: [Optional] Boosting Notebook - Part 3 </li><li>Practice Lab: Ada Boost </li><li>Practice Lab: Stacking For Classification with Python </li><li>Practice Lab: (Optional) Gradient Boosting </li><li>Practice Assignment: Boosting and Stacking Labs </li><li>Reading: Summary/Review </li><li>Module 5 Graded Quiz </li></ul><div> </div><div>Module 6: Modeling Unbalanced Classes </div><ul><li>Reading: Learning Objectives </li><li>Video: Model Interpretability </li><li>Video: Examples of Self-Interpretable and Non-Self-Interpretable Models </li><li>Video: Model-Agnostic Explanations </li><li>Video: Surrogate Models </li><li>Practice Lab: Model Interpretability </li><li>Practice Assignment:  Model interpretability </li><li>Video: Introduction to Unbalanced Classes </li><li>Video: Upsampling and Downsampling </li><li>Video: Modeling Approaches: Weighting and Stratified Sampling </li><li>Video: Modeling Approaches: Random and Synthetic Oversampling </li><li>Video: Modeling Approaches: Nearing Neighbor Methods </li><li>Video: Modeling Approaches: Blagging </li><li>Practice Lab: Modeling Imbalanced Classes </li><li>Practice Assignment: Modeling Unbalanced Classes </li><li>Reading: Summary/Review </li><li>Module 6 Graded Quiz </li><li>Peer Assignment: Course Final Project </li><li>Reading: Thanks from the Course Team </li></ul><div><br /></div><h1>General Information</h1><div>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari. <br /><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics. </div>
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Learn key supervised machine learning and classification techniques, including logistic regression, decision trees, ensemble methods, and handling unbalanced data. Build and evaluate powerful classification models using real-world datasets. 

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  <div>This course offers a practical introduction to regression, a fundamental technique in supervised machine learning. You’ll explore the full workflow of building regression models from data splitting and feature selection to preventing overfitting through best practices. </div><div><br />Gain hands-on experience training models that predict continuous outcomes and evaluating performance using key metrics like MAE, MSE, and RMSE. Dive deep into linear regression and apply regularization methods such as Ridge, LASSO, and Elastic Net to enhance model accuracy and generalization. </div><div><br />By the end of the course, you’ll be able to distinguish regression from classification problems, implement and interpret linear models, use error metrics for model comparison, and choose the most effective regression approach for your data. </div><div><br />Perfect for aspiring data scientists and machine learning engineers, this course equips you with the technical and analytical skills needed to apply regression techniques to real-world problems. <br /><br /></div><h1>Learning Objectives</h1><div>• Build and test regression models to predict continuous numerical values. </div><div>• Assess model accuracy using performance metrics like MAE, MSE, and RMSE. </div><div>• Use regularization methods like Ridge, LASSO, and Elastic Net to reduce overfitting. </div><div>• Evaluate and compare models to choose the best fit for real-world tasks. <br /><br /></div><h1>Course Syllabus</h1><div>Reading: Course Prerequisites </div><div>Video: Welcome/Introduction Video <br /><br />Module 1: Introduction to Supervised Machine Learning and Linear Regression </div><ul><li>Reading: Learning Objectives </li><li>Video: Introduction to Supervised Machine Learning - Types of Machine Learning (Part 1) </li><li>Video: Introduction to Supervised Machine Learning - Types of Machine Learning (Part 2) </li><li>Video: Supervised Machine Learning (Part 1) </li><li>Video: Supervised Machine Learning (Part 2) </li><li>Video: Regression and Classification Examples </li><li>Practice Assignment: Practice Quiz: Introduction to Supervised Machine Learning </li><li>Video: Introduction to Linear Regression (Part 1) </li><li>Video: Introduction to Linear Regression (Part 2) </li><li>Video: (Optional) Linear Regression Demo – Part 1 </li><li>Video: (Optional) Linear Regression Demo – Part 2 </li><li>Video: (Optional) Linear Regression Demo – Part 3 </li><li>App Item: Demo Lab: Linear Regression </li><li>App Item: Practice Lab: Linear Regression </li><li>Practice Assignment: Practice Quiz: Linear Regression </li><li>Reading: Summary/Review </li><li>Graded Assignment: Module 1 Graded Quiz: Introduction to Supervised Machine Learning and Linear Regression </li></ul><div>Module 2: Data Splits and Polynomial Regression </div><ul><li>Reading: Learning Objectives </li><li>Video: Training and Test Splits (Part 1) </li><li>Video: Training and Test Splits (Part 2) </li><li>App Item: Demo Lab: Training and Test Splits </li><li>Video: (Optional) Training and Test Splits Lab - Part 1 </li><li>Video: (Optional) Training and Test Splits Lab - Part 2 </li><li>Video: (Optional) Training and Test Splits Lab - Part 3 </li><li>Video: (Optional) Training and Test Splits Lab - Part 4 </li><li>Practice Assignment: Practice Quiz: Training and Test Splits </li><li>Video: Polynomial Regression </li><li>App Item: Practice Lab: Polynomial Regression </li><li>Practice Assignment: Practice Quiz: Polynomial Regression </li><li>Reading: Summary/Review </li><li>Graded Assignment: Module 2 Graded Quiz: Data Splits and Polynomial Regression </li></ul><div>Module 3: Cross Validation </div><ul><li>Reading: Learning Objectives </li><li>Reading: Important Note on Labs and Videos </li><li>Video: Cross Validation - Part 1 </li><li>Ungraded Plugin: Reading: K-Fold Cross-Validation </li><li>Video: Cross Validation Demo - Part 1 </li><li>Video: Cross Validation Demo - Part 2 </li><li>Video: Cross Validation Demo - Part 3 </li><li>Video: Cross Validation Demo - Part 4 </li><li>Video: Cross Validation Demo - Part 5 </li><li>App Item: Demo Lab: Cross Validation </li><li>App Item: Practice Lab: Cross Validation </li><li>Practice Assignment: Practice Quiz: Cross Validation </li><li>Reading: Summary/Review </li><li>Graded Assignment: Graded: Module 3 Quiz:  Cross Validation </li></ul><div>Module 4:  </div><ul><li>Reading: Learning Objectives </li><li>Video: Bias Variance Trade off (Part 1) </li><li>Video: Bias Variance Trade off (Part 2) </li><li>Video: Regularization and Model Selection </li><li>Video: Ridge Regression </li><li>Video: Lasso Regression (Part 1) </li><li>Video: Lasso Regression (Part 2) </li><li>Video: Elastic Net </li><li>Practice Assignment: Practice Quiz: Regularization Techniques </li><li>App Item: Demo Lab: Polynomial Features and Regularization </li><li>Video: Polynomial Features and Regularization Demo - Part 1 </li><li>Video: Polynomial Features and Regularization Demo - Part 2 </li><li>Video: Polynomial Features and Regularization Demo - Part 3 </li><li>Practice Assignment: Practice Quiz: Polynomial Features and Regularization </li><li>Reading: Summary/Review </li><li>Graded Assignment: Module 4 Graded Quiz: Bias Variance Trade off and Regularization Techniques: Ridge, LASSO, and Elastic Net </li></ul><div>Module 5: Regularization Details </div><ul><li>Reading: Learning Objectives </li><li>Video: Further details of regularization - Part 1 </li><li>Video: Further details of regularization - Part 2 </li><li>App Item: Demo Lab: Details of Regularization </li><li>Video: (Optional) Details of Regularization - Part 1 </li><li>Video: (Optional) Details of Regularization - Part 2 </li><li>Video: (Optional) Details of Regularization - Part 3 </li><li>App Item: Practice Lab: Regularization </li><li>Practice Assignment: Practice Quiz: Details of Regularization </li><li>Reading: Summary/Review </li><li>Graded Assignment:  Module 5 Graded Quiz: Regularization Details </li></ul><div>Module 6: Final Project </div><ul><li>Reading: Project Scenario </li><li>Hands-on Lab: Final Project </li><li>Final Project Submission and Evaluation </li><li>Congratulations and Next Steps </li><li>Reading: Thanks from the Course Team </li></ul><h1>General Information</h1><div>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari. <br /><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics. </div>
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Machine Learning: Regression

Learn key supervised machine learning techniques in regression analysis. Train and test regression models to build predictive insights that are ideal for aspiring data scientists and machine learning engineers. 

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  <div>Launch your Machine Learning journey with this introductory course, part of the IBM Machine Learning Professional Certificate. Build a solid foundation in key ML concepts and discover the importance of clean, high-quality data. Gain hands-on experience retrieving data from diverse sources like SQL and NoSQL databases, APIs, and cloud platforms. <br /><br /></div><div>Learn essential data preparation techniques, including handling missing values, encoding categorical and ordinal features, detecting and managing outliers, and applying feature engineering and selection methods. Explore feature scaling techniques and understand their impact on model performance. <br /><br /></div><div>By the end of this course, you’ll be able to prepare robust datasets for analysis and hypothesis testing that are crucial skills for real-world Machine Learning projects. Designed for aspiring data scientists, ML professionals, and AI Engineers this course offers practical, industry-relevant experience. <br /><br /></div><h1>Learning Objectives </h1><div>• Retrieve data from various sources—SQL/NoSQL databases, APIs, and cloud platforms—for machine learning tasks. </div><div>• Clean and prepare data by handling missing values, encoding categories, and managing outliers. </div><div>• Apply feature engineering, selection, and scaling to optimize model performance. </div><div>• Prepare high-quality datasets for analysis, hypothesis testing, and real-world ML projects. <br /><br /></div><h1>Course Syllabus</h1><div>Module 1: A Brief History of Modern AI and its Applications </div><ul><li>Reading: Learning Objectives </li><li>Video: Course Introduction </li><li>Reading: Course Prerequisites </li><li>Video: Introduction to Artificial Intelligence and Machine Learning </li><li>Video: Machine Learning and Deep Learning </li><li>Video: Machine Learning And Deep Learning Part 1 </li><li>Video: Machine Learning and Deep Learning - Part 2 </li><li>Video: History of AI </li><li>Video: History of Machine Learning and Deep Learning </li><li>Practice Quiz: Artificial Intelligence and Machine Learning </li><li>Video: Modern AI  </li><li>Video: Applications </li><li>Optional: Say hi or reach out for help </li><li>Video: Machine Learning Workflow </li><li>Practice Quiz: Modern AI Applications and Workflows </li><li>End of the module review &amp; evaluation </li><li>Reading: Review </li><li>Graded Quiz: Module 1 - Modern AI and its Applications </li></ul><div> </div><div>Module 2: Retrieving and Cleaning Data </div><ul><li>Module Summary and Learning Objectives </li><li>Video: Retrieving Data from CSV and JSON Files </li><li>Video: Retrieving Data from Databases, APIs, and the Cloud </li><li>Demo Lab: Reading Data in Database Files - Part A </li><li>Reading: [Optional] Download Assets for Lab: Reading Data in Database Files - Part A </li><li>Video: [Optional] Lab Solution: Reading Data Jupyter Notebook - Part A </li><li>Demo Lab: Reading Data in Jupyter Notebook - Part B </li><li>Reading: [Optional] Download Assets for Lab: Reading Data in Jupyter Notebook - Part B </li><li>Video: [Optional]Lab Solution: Reading in Database Files - Part B </li><li>Practice Quiz: Retrieving Data </li><li>Video: Data Cleaning </li><li>Video: Handling Missing Values and Outliers </li><li>Video: Handling Missing Values and Outliers using Residuals </li><li>Practice Lab: Data Cleaning </li><li>Practice Quiz: Data Cleaning </li><li>Reading: Summary/Review </li><li>Graded Quiz: Module 2 - Retrieving Data and Cleaning Data </li></ul><div> </div><div>Module 3:  </div><ul><li>Module Summary and Learning Objectives </li><li>Video: Introduction to Exploratory Data Analysis (EDA) </li><li>Video: EDA with Visualization </li><li>Video: Grouping Data for EDA </li><li>App Item: Demo Lab: Exploratory Data Analysis </li><li>Reading: [Optional] Download Assets for Lab: Exploratory Data Analysis Lab </li><li>Video: [Optional]Solution: EDA Notebook - Part 1 </li><li>Video: [Optional]Solution: EDA Notebook - Part 2 </li><li>Video: [Optional]Solution: EDA Notebook - Part 3 </li><li>Video: [Optional]Solution: EDA Notebook - Part 4 </li><li>Practice Lab:  Exploratory Data Analysis </li><li>Practice Quiz: Exploratory Data Analysis </li><li>Video: Feature Engineering and Variable Transformation – Background </li><li>Video: Variable Transformation </li><li>Video: Feature Encoding </li><li>Video: Feature Scaling </li><li>Video: Common Variable Transformations in Python </li><li>Demo Lab: Feature Engineering </li><li>Reading: [Optional] Download Assets for Lab: Feature Engineering Demo </li><li>Video: [Optional] Solution: Feature Engineering Lab - Part 1 </li><li>Video: [Optional] Solution: Feature Engineering Lab - Part 2 </li><li>Video: [Optional] Solution: Feature Engineering Lab - Part 3 </li><li>Practice Lab:  Feature Engineering </li><li>Practice Quiz: Feature Engineering and Variable Transformation </li><li>Reading: Summary/Review </li><li>Graded Quiz: Module 3 - Exploratory Data Analysis and Feature Engineering </li></ul><div>Module 4:  </div><ul><li>Video: Estimation and Inference - Introduction </li><li>Video: Estimation and Inference – Example </li><li>Video: Estimation and Inference - Parametric vs. Non-Parametric </li><li>Video: Estimation and Inference - Commonly Used Distributions </li><li>Video: Frequentist vs. Bayesian Statistics </li><li>Practice Quiz: Estimation and Inference, and Hypothesis Testing </li><li>Video: Introduction to Hypothesis </li><li>Video: Hypothesis Testing Example </li><li>Video: Bayesian Interpretation of Hypothesis Testing Example </li><li>Video: Type 1 vs Type 2 Error </li><li>Video: Type 1 vs Type 2 Error: Examples </li><li>Video: Hypothesis Testing Terminology </li><li>Video: Significance Level and P-Values </li><li>Video: Significance Level and P-Values and the F Statistic </li><li>Demo Lab: Hypothesis Testing </li><li>Reading: [Optional] Download Assets for Lab: Hypothesis Testing Demo </li><li>Video: [Optional] Hypothesis Testing Demo - Part 1 </li><li>Video: [Optional] Hypothesis Testing Demo - Part 2 </li><li>Video: Correlation vs Causation </li><li>Practice Lab:  Hypothesis Testing </li><li>Practice Quiz: Hypothesis Testing </li><li>Discussion Prompt: Optional Brainstorming </li><li>Reading: Summary/Review </li><li>Reading: Summary/Review </li><li>Graded Quiz: Module 4 - Inferential Statistics and Hypothesis Testing </li></ul><div> </div><div>Module 5:  </div><ul><li>Module Summary and Learning Objectives </li><li>Reading: Project Overview </li><li>Reading: Submission Guidelines </li><li>Final Project Submission and Evaluation </li><li>Reading: Congratulations &amp; Next Steps </li><li>Reading: Thanks from the Course Team </li></ul><h1>General Information</h1><div>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari. <br /><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics.</div>
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Machine Learning: Exploratory Data Analysis

Build essential, employer-valued skills for machine learning and AI success. Master data preparation, retrieval, and cleaning using real-world tools and techniques. 

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  <div>Have you ever wished you could sit down for a realistic job interview, which is tailored exactly to your resume and the role you want, without scheduling with a recruiter, asking a friend for help, or feeling the pressure of a real interview?<br /><br /></div><div>Interview preparation is one of the most important steps in landing a job, but it’s often stressful and uncertain. What questions will they ask? How do your past experiences align with the role? How can you practice speaking confidently and clearly? Traditional prep tools rarely adapt to your personal background, and most mock interviews feel scripted or generic.<br /><br /></div><div>What if an AI agent could read your resume, understand the job you’re applying for, and conduct a personalized, dynamic interview? In this project, you’ll build an AI-powered interview coach that does exactly that. You’ll integrate resume parsing, job-description analysis, natural language generation, speech synthesis, and speech-to-text transcription into one seamless pipeline. Your system will read PDFs, summarize key strengths, generate tailored interview questions, convert them into natural interviewer audio, record the user’s voice responses, transcribe them, and continuously evaluate performance. Everything culminates in a polished, interactive Gradio web interface that lets anyone rehearse for a job interview anytime, anywhere.</div><div><br /></div><h1>What You’ll Learn</h1><div>By the end of this project, you will be able to:</div><ul><li><strong>Extract and understand resume content automatically:</strong> Use PyPDF2 to parse resume PDFs and generate structured summaries to inform the interview.</li><li><strong>Analyze job descriptions with LLM reasoning:</strong> Build a job description expert agent that interprets role expectations, required skills, and hiring priorities.</li><li><strong>Generate intelligent, contextual interview questions:</strong> Use a powerful LLM to ask adaptive questions based on both the resume and prior answers.</li><li><strong>Convert interviewer prompts into speech:</strong> Implement text-to-speech with gTTS to create natural-sounding, interactive audio-based interviews.</li><li><strong>Capture and transcribe user responses:</strong> Use the Faster Whisper model for high-quality speech-to-text transcription of microphone input.</li><li><strong>Deliver real-time evaluation:</strong> Build a custom evaluation agent that summarizes performance, strengths, and areas for improvement.</li><li><strong>Develop a full Gradio app:</strong> Create a smooth, shareable interface that ties all components together—upload, listen, respond, and receive feedback.</li></ul><h1>Who Should Enroll</h1><div>This project is perfect for:</div><ul><li><strong>AI enthusiasts and developers</strong> interested in combining language models, audio processing, and user-facing interfaces.</li><li><strong>Job seekers and career coaches</strong> who want to harness AI to create personalized interview training tools.</li><li><strong>Students and researchers</strong> looking to build multi-step AI systems that integrate text, speech, and interactive workflows.</li><li><strong>Educators</strong> who want a hands-on project demonstrating real-world applications of LLMs and voice interfaces.</li></ul><h1>Why Enroll</h1><div>This project brings the worlds of career development, conversational AI, and multimodal interaction together. Instead of guessing what interviewers might ask—or searching through lists of generic practice questions—you’ll learn to build a tool that understands your background and adapts to your needs. With your AI interview coach, every question, follow-up, and evaluation will be custom-made for the role you’re targeting.</div><div>By the end, you’ll have:</div><ul><li>A fully functional AI interview simulation tool.</li><li>Practical experience integrating text, audio, and LLM workflows.</li><li>A deeper understanding of how generative AI can transform professional skill-building.</li><li>A portfolio-ready project demonstrating real-world relevance and engineering depth.</li></ul><h1>What You’ll Need</h1><div>To get the most out of this project, you should have:</div><ul><li>Basic Python programming skills.</li><li>Some familiarity with AI concepts (LLMs, speech processing, etc.)—helpful but optional.</li><li>Curiosity about how generative AI can enhance professional training and personal development.</li></ul><div>All required libraries are installed in the environment, and the web app works best on the latest versions of Chrome, Firefox, Edge, or Safari.</div>
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Build Your Interview Coach with Llama, OpenAI Whisper & TTS

Build an LLM-powered interview coach that analyzes resumes, reads job descriptions, and conducts personalized mock interviews with OpenAI Whisper speech tool. Extract and summarize resume content automatically, interpret job requirements, and generate tailored interview questions that adapt to the user’s responses in real time. Convert interviewer prompts into natural-sounding audio, record the user’s spoken answers, and transcribe them with Whisper for deeper evaluation. By the end, you’ll build a web app simulating a realistic interview experience, helping you ace interviews!

<div>This program equips you with the practical, job-ready skills used by successful ethical hackers — all in just 2-3 months. Ethical hacking involves authorized testing of computer systems to uncover security weaknesses before malicious actors can exploit them.&nbsp;<br><br></div><div>In this Professional Certificate, you will:&nbsp;</div><ul><li>Define key ethical and legal principles governing ethical hacking and responsible disclosure practices.</li><li>Explain the roles, responsibilities, and ethical distinctions between white hat, gray hat, and black hat hackers.</li><li>Apply Kali Linux tools to perform reconnaissance, vulnerability assessment, and exploitation within controlled environments.</li><li>Demonstrate the use of Metasploit and OpenVAS to execute controlled exploits and vulnerability scans.</li><li>Use Wireshark to evaluate system and network vulnerabilities by interpreting penetration test results and prioritizing risk mitigation strategies.</li><li>Create professional security assessment reports that document findings, ethical considerations, and recommended remediation actions.</li></ul><div>&nbsp;</div><div><strong>Program Overview</strong>&nbsp;</div><div>&nbsp;</div><div>The demand for cybersecurity professionals continues to rise, with information security jobs projected to grow 32% by 2032 (U.S. Bureau of Labor Statistics). As ethical hacking becomes an essential part of modern cybersecurity strategies, skilled ethical hackers are increasingly sought after to safeguard organizations from evolving threats.<br><br></div><div>Through structured labs and real-world projects, you’ll learn how to identify, assess, and mitigate vulnerabilities to strengthen digital defenses.</div><div>You’ll gain hands-on experience with industry-standard open-source tools such as Kali Linux, Metasploit, Wireshark and OpenVAS, while also exploring the ethical and legal foundations of cybersecurity practice. Under the guidance of IBM cybersecurity experts, you’ll learn to conduct controlled penetration tests, perform ethical exploitations, and develop effective incident response strategies.</div><div>By the end of the program, you’ll have built a professional portfolio demonstrating your ability to perform ethical hacking tasks and communicate findings clearly — preparing you for roles such as Ethical Hacker, Penetration Tester, Security Analyst, or Incident Responder.</div><div><br><strong>Applied Learning Projects:</strong></div><div>Throughout the program, you’ll complete hands-on projects simulating real-world cybersecurity scenarios. These projects help you apply theory to practice and develop a well-rounded skillset in ethical hacking.<br><br></div><div>Key projects include:</div><ul><li>Perform controlled penetration tests in virtual environments using Kali Linux&nbsp;</li><li>Capture and analyze network traffic with Wireshark to identify vulnerabilities&nbsp;</li><li>Use Metasploit to execute ethical exploits in isolated systems&nbsp;</li><li>Run vulnerability scans with OpenVAS and develop structured incident response plans&nbsp;</li><li>Practice responsible reporting by documenting discovered vulnerabilities and following ethical disclosure procedures aligned with legal and organizational guidelines.&nbsp;</li><li>Explore how AI-powered tools can enhance threat detection, vulnerability analysis, and penetration testing efficiency in modern cybersecurity practices.&nbsp;</li></ul><div>&nbsp;</div><div>Each project enhances your technical and analytical capabilities, helping you confidently identify, analyze, and resolve security vulnerabilities in professional settings.<br><br></div><div>Whether you’re looking to launch a cybersecurity career or advance into a specialist role such as Ethical Hacker, Penetration Tester, Security Analyst, or Incident Responder, this program will equip you with the job-ready experience and portfolio to stand out in a competitive field.&nbsp;<br><br></div><div>You’ll also work with advanced tools and techniques used by professionals, including Wireshark, OpenVAS, Metasploit, and Kali Linux, to perform real-world security testing and analysis.&nbsp;<br><br></div><div>Finally, through the Capstone project, you’ll explore how AI-powered tools can enhance threat detection, vulnerability assessment, and penetration testing efficiency, applying your skills in a realistic cybersecurity scenario.&nbsp;<br><br></div><div><strong>Enroll today</strong> to start building the ethical hacking skills that employers around the world are looking for.&nbsp;<br>&nbsp;</div><div><strong>Job Outlook</strong>&nbsp;</div><div>&nbsp;</div><div>There is a worldwide shortage of over 3 million skilled cybersecurity experts. (Source: ISC²).&nbsp;<br><br></div><div>With 93% of organizations experiencing two or more identity-related breaches in the past year, demand for risk management and cybersecurity skills is skyrocketing. (Source: Coursera Job Skills Report)&nbsp;<br><br></div><div>More than 50% of public organizations say a lack of resources and skills is their biggest obstacle to achieving cyber resilience. (Source: Coursera Job Skills Report)&nbsp;<br><br></div><div>Earning this certificate positions you for entry-level roles such as:&nbsp;</div><ul><li><strong>Ethical Hacker</strong>&nbsp;</li><li><strong>Penetration Tester</strong>&nbsp;</li><li><strong>Vulnerability Analyst</strong>&nbsp;</li><li><strong>Cybersecurity Analyst</strong>&nbsp;</li><li><strong>Security Operations Center (SOC) Specialist</strong>&nbsp;</li></ul><div><br></div><div>With IBM’s industry-recognized credential, you’ll demonstrate job-ready ethical hacking skills and the ability to protect systems using real-world, open-source tools. <br><br><strong>Prerequisites</strong>&nbsp;<br><br></div><div>Learners should have a basic understanding of IT and cybersecurity fundamentals before starting this course. You will also need access to a desktop or laptop computer capable of running Kali Linux and other cybersecurity tools used throughout the hands-on activities. The following are requirements for the Professional Certificate:<br><br></div><ul><li>This Professional Certificate requires fundamental knowledge of IT concepts and basic cybersecurity principles.&nbsp; The program starts with fundamentals and progressively builds more advanced skills using open-source cybersecurity tools.</li><li>To complete the hands-on exercises in this Professional Certificate, you will need your own desktop or laptop computer capable of running virtualization software and supporting the cybersecurity tools used in the course. You must install several tools, including Kali Linux (via virtualization), Wireshark, Metasploit, and OpenVAS. A reliable internet connection is also required to download these tools and access course materials. Chromebooks, tablets, or systems without virtualization capabilities will not be sufficient for completing the practical components of this program.</li></ul>

IBM Ethical Hacking with OS Tools Professional Certificate 

Master key ethical hacking tools in just 2-3 months. Explore Kali Linux, Wireshark, Metasploit, OpenVAS, and Gen AI for penetration testing, vulnerability assessment, and incident response.

<div>Get ready to launch your systems analyst career with the job-ready skills hiring managers are looking for! This IBM Professional Certificate gives you the essential skills in system analysis, design, business process modeling, stakeholder management, requirements gathering, and data visualization that you need to get your first systems analysis role.&nbsp;</div><div>&nbsp;</div><div><strong>You will learn how to:</strong>&nbsp;</div><div>&nbsp;</div><ul><li>Analyze, design, and develop scalable enterprise system architectures using modeling tools, and agile methodologies.&nbsp;</li><li>Analyze, design, and document IT systems using tools such as data flow diagrams, entity-relationship diagrams, and UML &nbsp;</li><li>Develop working knowledge of IT systems design, analysis, software testing, deployment, and maintenance strategies. &nbsp;</li><li>Gather, document, and validate business and technical needs while engaging stakeholders to ensure alignment and successful outcomes.&nbsp;</li></ul><div><strong>Program overview</strong>&nbsp;</div><div>&nbsp;</div><div>Systems analysts play a key role in evaluating, designing, and improving computer systems and business processes to boost organizational efficiency. &nbsp;</div><div>In this program, you’ll learn how to identify business needs, map workflows, and design impactful IT solutions. And you’ll get hands-on using data visualization tools, such as Excel, Cognos, and data modeling techniques—all while developing documentation and visualization skills. &nbsp;</div><div>&nbsp;</div><div>You’ll also discover how systems analysts support agile teams, drive change management initiatives, and align stakeholders through effective communication and risk management. Plus, you’ll dive into IT system design, testing, and maintenance, and then you will work on a capstone project to demonstrate your learning from this program. &nbsp;</div><div>&nbsp;</div><div>If you’re keen to launch your career in systems analysis with confidence, enroll today and power up your resume with the in-demand skills employers are looking for!&nbsp;</div><div>&nbsp;</div><div><strong>Your job outlook</strong>&nbsp;</div><div>&nbsp;</div><div>Demand for systems analysts is growing much faster than the average for all occupations (US Bureau of Labor Statistics). So, with the right skills, you have a lot to look forward to in your career.&nbsp;</div><div>&nbsp;</div><ul><li>According to the US Bureau of Labor Statistics, employment of systems analysts is projected to grow 9 percent till 2034.&nbsp;</li><li>Any organization that depends on technology to function can require the expertise of a systems analyst—including tech companies, financial institutions, healthcare organizations, government agencies, retail businesses, and manufacturing enterprises.&nbsp;</li></ul><div><br><strong>Prerequisites</strong>&nbsp;</div><div>&nbsp;</div><div>To get the most from this program, you need to have computer literacy .&nbsp;</div>

IBM Systems Analyst Professional Certificate

Build the job-ready systems analyst skills employers need in 4 months. Master process modeling, requirements gathering, stakeholder management and much more. 

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  <div>Hello, and welcome to the Systems Analyst Capstone Project course!<br /><br /></div><div>This capstone course is designed for aspiring and current systems analysts ready to apply their knowledge and skills to solve real-world business challenges. Through this comprehensive project-based course, you will take on the role of a Systems Analyst for XYZ Health Solutions, a mid-sized healthcare provider facing operational inefficiencies and fragmented systems. You will design and implement a unified IT solution that addresses critical challenges in patient records, appointment scheduling, and billing processes.<br /><br /></div><div>This capstone brings together all the essential skills you’ve developed throughout the program, from core systems concepts to process modeling and requirements gathering, stakeholder management and project planning, data visualization and dashboards, IT fundamentals, systems design and analysis, and software testing, deployment, and maintenance.<br /><br /></div><div>Whether launching your systems analyst career or advancing your current role, this capstone provides hands-on experience with the tools, techniques, and methodologies used by professional systems analysts in today's technology-driven business environment.</div><div><br />This course is the culmination of the <a href="https://www.coursera.org/professional-certificates/ibm-systems-analyst/">IBM Systems Analyst Professional Certificate</a> program. The other courses in this certificate program include:</div><ul><li><a href="https://www.coursera.org/learn/introduction-to-systems-analysis?specialization=ibm-systems-analyst">Introduction to Systems Analysis</a></li><li><a href="https://www.coursera.org/learn/business-analysis-process-modeling-requirements-gathering?specialization=ibm-systems-analyst">Business Analysis: Process Modeling &amp; Requirements Gathering</a></li><li><a href="https://www.coursera.org/learn/business-analysis-project-and-stakeholder-management?specialization=ibm-systems-analyst">Business Analysis: Project and Stakeholder Management</a></li><li><a href="https://www.coursera.org/learn/data-visualization-dashboards-excel-cognos?specialization=ibm-systems-analyst">Data Visualization and Dashboards with Excel and Cognos</a></li><li><a href="https://www.coursera.org/learn/information-technology-it-fundamentals-for-everyone?specialization=ibm-systems-analyst">Information Technology (IT) Fundamentals for Everyone</a></li><li><a href="https://www.coursera.org/learn/it-systems-design-and-analysis?specialization=ibm-systems-analyst">IT Systems Design and Analysis</a></li><li><a href="https://www.coursera.org/learn/software-testing-deployment-and-maintenance-strategies?specialization=ibm-systems-analyst">Software Testing, Deployment, and Maintenance Strategies</a></li><li>Generative AI for Systems Analyst (Coming soon)</li><li>Practice Exam for CISA Certification (Coming soon)</li></ul><div>Follow the links above to learn more about the complete program and see how these courses can benefit you and advance your career as a systems analyst.<br /><br /></div><h1>Course Syllabus</h1><div>Module 1: Project Initiation and Requirements Gathering</div><div>Module 2: Project Planning and Process Modeling</div><div>Module 3: Data Preparation and Visualization</div><div>Module 4: System and Database Design</div><div>Module 5: System Testing and Deployment Strategies</div><div>Module 6: Final Submission and Evaluation</div><div>Bonus: Career Guidance and Next Steps</div><div><br /></div><h1>Course objectives</h1><div>After completing this course, you will be able to:</div><div>Build job-ready systems analysis skills to design IT solutions that enhance operations and data management</div><div>Create process models, Entity-Relationship Diagrams (ERDs), and interactive dashboards to visualize and optimize workflows</div><div>Implement data quality, security, and compliance measures aligned with standards and regulations</div><div>Deliver a comprehensive IT solution that addresses stakeholder needs and supports data-driven decision-making</div><div>Develop professional presentation skills to communicate technical solutions to diverse business stakeholders<br /><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>This capstone course is designed for learners who have completed all the courses in the IBM Systems Analyst Professional Certificate program. To get the most out of your learning experience, you should have some background in these topics:</div><ul><li>Stakeholder engagement techniques and requirements elicitation methods</li><li>Requirements documentation using Business Requirements Documents (BRD) and System Requirements Specifications (SRS)</li><li>Process mapping and flowcharts using tools like Lucidchart</li><li>Basic data analysis and visualization using Excel and analytics platforms</li><li>Fundamental principles of system design and database concepts</li><li>Project management methodologies and risk assessment techniques</li></ul>
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Systems Analyst Capstone Project

During this project, you’ll apply systems analysis methodologies to streamline business processes, data management, and system integration. Through hands-on labs, you’ll use requirements gathering techniques, create process models using UML diagrams, design relational databases, and build interactive dashboards using Tableau or Cognos.As you work, you’ll demonstrate your end-to-end systems analysis skills in stakeholder management, data security, and meeting compliance requirements.

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  <div>Explain to a potential learner why this course is important/interesting and what they can gain from taking your course.<br /><br /></div><h1>Course Syllabus</h1><div>Module 1 Develop a Recycling Tracker</div><div>Module 2 Build the PetCare Scheduler </div><div><br /></div><h1>Prerequisites</h1><div>To get the most out of this course, you should be comfortable with the following Java-related tools and programming techniques:</div><ul><li>Use the Java Development Kit (JDK), Java Runtime Environment (JRE), and an Integrated Development Environment (IDE).</li><li>Implement data types, variables, operators, and control flows.</li><li>Work with arrays, strings, methods, and implement robust exception handling.</li><li>Apply object-oriented programming (OOP) principles to define and implement classes and objects, using encapsulation and abstraction.</li><li>Use advanced OOP concepts such as inheritance, polymorphism, interfaces, and method overloading.</li><li>Apply the Java Collections Framework to manage data with lists, sets, queues, and maps.</li><li>Perform basic file and directory operations.</li><li>Implement commonly used techniques for handling dates and times in Java.</li></ul><div>If you need these skills, these courses offer the experience you need for success in this course<br /><br />Course 1:<br />Java for Beginners<br /><br />Course 2:<br />Object Oriented Programming in Java</div><div><br /></div><h1>Course Objectives:</h1><div>After completing this single-module course, you will be able to:</div><ul><li>Design and build modular Java applications using object-oriented programming (OOP), classes, and collections.</li><li>Develop console-based Java systems that support user interaction, data persistence, and file handling.</li><li>Implement exception handling in Java to manage input errors, enforce data integrity, and handle file operations.</li><li>Generate formatted reports from object data using Java filtering and display techniques.</li></ul><div>In this hands-on course, you'll put your Java skills into action by building two prototype console-based applications, a recycling tracker, and a pet care appointment system. You'll apply object-oriented programming to design modular code, work with ArrayLists and HashMaps to manage data, and use file I/O to store and retrieve information. You'll practice handling exceptions, validating user input, and generating clear, formatted reports. By the end, you'll have developed working console-based applications you can share with employers to showcase your growing Java expertise.</div>
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Java App Development Project: Fundamentals, OOP & File I/O

Boost your Java development skills with curated projects designed to help you quickly build real-world experience.   Through online project-based labs, you'll apply critical development techniques that employers expect a good Java developer to know.  You'll gain hands-on experience coding console-based Java applications, practice your skills working with a console-based recycling tracker, and prove your capabilities building a pet appointment management system that incorporates object-oriented programming (OOP), Java collections including ArrayLists and HashMaps.

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  <div>Ready to take your Java skills to the next level? This course bridges the gap between basic Java programming and enterprise-level software engineering. You’ll build valuable enterprise Java development skills founded on modern patterns and practices that make your resume stand out!<br /><br />During the course, you'll master design patterns using lambda expressions, implement Test-Driven Development (TDD) with JUnit 5 and Mockito, and deploy scalable applications using Docker and Kubernetes.<br /><br />In hands-on labs and real-world scenarios, you'll learn Gang of Four patterns modernized for Java 17+, comprehensive testing strategies, and cloud-native deployment approaches that top tech companies use daily. Plus, through a project-based approach, you’ll combine three critical enterprise skills: design pattern implementation, test-driven development, and containerization practices, building a complete inventory management system. A great addition to your portfolio that you can talk about in interviews! <br /><br />If you’re keen to build more advanced Java development skills employers need for Java developer roles, enroll today!<br /><br /></div><h1>Course Syllabus</h1><div><strong>Module 1: Modern Java Design Patterns</strong></div><div><strong>Module 2: Testing with Java</strong></div><div><strong>Module 3: Deployment Practices with Java</strong></div><div><strong>Module 4: Final Project and Course Wrap-Up</strong></div><div><br /></div><h1>General Information</h1><div>After completing this course, you will be able to: </div><ul><li>Design tests for extensible Java 17+ applications using factory, strategy, and observer patterns and apply them to solve real-world problems. </li><li>Implement and assess modern design patterns with Java features, including lambdas, functional interfaces, and modular architecture. </li><li>Build robust Java test suites using TDD, JUnit 5, AssertJ, and Mockito to ensure unit and integration test coverage for microservices. </li><li>Deploy secure, cloud-ready Java apps using Docker, Maven, and CI/CD pipelines with best practices in dependency management. </li></ul><div><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>This course is recommended for learners who have foundational software engineering skills, Java programming and object-oriented programming skills, working knowledge of Git and GitHub, and familiarity with cloud-native concepts. </div><div><br />If you don't yet have these skills, the following courses provide the knowledge and skills you need to successfully complete this course: <br /><br /></div><ul><li><a href="https://www.coursera.org/learn/introduction-to-software-engineering">Introduction to Software Engineering </a></li><li><a href="https://www.coursera.org/learn/java-for-programming-beginners">Java Programming for Beginners </a></li><li><a href="https://www.coursera.org/learn/getting-started-with-git-and-github">Getting Started with Git and GitHub</a></li><li><a href="https://www.coursera.org/learn/object-oriented-program-in-java">Object Oriented Programming in Java </a></li><li><a href="https://www.coursera.org/learn/cloud-native-microservices-containers-devops-agile?specialization=java-developer">Cloud Native, Microservices, Containers, DevOps, and Agile</a></li></ul><div><br /></div><div>You will need a laptop or desktop with internet access to access the course materials. </div>
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Java: Design Patterns, Testing and Deployment

In this course, you'll learn how to design flexible Java applications using factory, strategy, and observer patterns to solve real-world problems. You'll discover how to apply modern Java 17+ features such as lambdas, functional interfaces, and modular architecture to write clean, maintainable code. Then learn how to build strong test suites for test-driven development (TDD) using JUnit 5, AssertJ, and Mockito to catch bugs early and help ensure reliability. Then, explore how you can deploy applications to production using Docker, Maven, and CI/CD pipelines. 

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  <div>Build job-ready cybersecurity skills through practical experience in incident response and vulnerability assessment using OpenVAS.  <br /><br /></div><div>Designed for cybersecurity professionals and aspiring ethical hackers, this course immerses you in realistic lab environments where you’ll install and configure OpenVAS, run scans, interpret CVSS scores, and identify vulnerabilities across networked systems.  <br /><br /></div><div>As you progress, you’ll learn to detect threats by analyzing system and network logs, correlating scan results with known attack patterns, and applying proven containment strategies. You’ll go beyond just technical execution and learn to prioritize risks, recommend remediation actions, and document your findings in structured, professional reports.  <br /><br /></div><div>You’ll also integrate threat intelligence and lessons learned into actionable post-incident improvements.  <br /><br /></div><div>A final hands-on project simulates a full incident response lifecycle from detection to reporting—great for job interviews!   <br /><br /></div><div>With guided labs, quizzes, and a final assignment, this course builds real-world readiness and adds a valuable credential to your cybersecurity portfolio.  <br /><br /></div><div>Ready to dive in and defend against real-world threats? Enroll today. <br /><br /></div>
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Incident Response and Defense with OpenVAS

This course provides a hands-on, structured approach to incident response and defense activities using OpenVAS. Learners will gain the skills to configure, operate, and analyze vulnerability scans, detect threats, prioritize risks, and respond effectively to cybersecurity incidents. Through interactive videos, readings, labs, and assessments, learners will develop essential operational cybersecurity skills required in real-world environments.

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  <div>Prepare to get hands-on using tools and strategies to design, analyze, and evaluate IT systems with confidence! During this course, you’ll explore how to connect technology with business needs and dive into practical projects using techniques such as data flow diagrams, entity-relationship diagrams, and Unified Modeling Language (UML) to map and improve real-world systems. </div><div><br />As you progress, you'll investigate how system architecture, technical workflows, and enterprise tools such as ERP and CRM shape business operations. You'll sharpen your ability to spot inefficiencies, evaluate solutions, and assess feasibility and risk to support smart decision-making. Plus, by combining structured and object-oriented design methods with business process modeling, you'll strengthen your skills to turn user needs into effective, workable solutions. <br /><br />Whether you're starting a career in systems analysis or enhancing your current IT expertise, this course will help you become a key contributor in delivering scalable, user-focused technology solutions. <br /><br />Enroll today and power up your resume with the IT systems design and analysis skills businesses are actively looking for!<br /><br /></div><h1>Course Syllabus</h1><div><strong>Module 1: System Design and Modeling</strong></div><div><strong>Module 2: IT Systems Analysis and Review</strong></div><div><strong>Module 3: Solution Evaluation and Feasibility Analysis</strong></div><div><strong>Module 4: Guided Project, Final Assessment, and Course Wrap-Up</strong></div><div><br /></div><h1><strong>Objectives</strong></h1><ul><li>Create data flow diagrams, entity-relationship diagrams, and unified modeling language diagrams to visualize data movement, database structures, system architecture, and workflows </li><li>Evaluate existing IT systems by assessing system architecture, features, performance, and UI/UX to identify inefficiencies and propose enhancements for improved functionality and business alignment </li><li>Conduct cost-benefit analyses, operational feasibility studies, and risk assessments to compare IT solution alternatives and recommend optimal, financially viable, and operationally sustainable solutions </li><li>Integrate system analysis, modeling, and evaluation techniques in a comprehensive project to propose a digital transformation strategy for a real-world business </li></ul><div><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>This course is recommended for professionals looking to develop their skills as system analysts, IT professionals, or business analysts. A grounding in fundamental IT concepts, such as databases, software development, or basic programming, is recommended. <br /><br /></div><div>If you need to build your knowledge in these areas before starting this course, the following resources can help:</div><div><br /><a href="https://www.coursera.org/professional-certificates/ibm-business-analyst-professional-certificate">IBM Business Analyst Professional Certificate</a></div><div><br /><a href="https://www.coursera.org/learn/introduction-software-programming-and-databases">Introduction to Software, Programming, and Databases<br /></a><br /></div>
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IT Systems Design and Analysis

This course is designed for current or aspiring systems analysts, IT managers and professionals, and business analysts looking to develop skills in implementing IT systems. This course equips you with the knowledge and skills to analyze, design, and implement IT systems and connect business needs with technical solutions. You will explore how the right tools will help you strengthen your IT experience and deliver user-centered IT solutions.

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  <div>This course provides hands-on training in network traffic analysis using Wireshark for cybersecurity professionals. You’ll learn to capture, analyze, and interpret network traffic to detect security threats and investigate incidents. Through practical exercises, you’ll gain experience with packet sniffing, protocol analysis, and traffic flow monitoring while working with real-world network data.<br /><br /></div><div>The curriculum covers essential techniques including Deep Packet Inspection (DPI), traffic filtering, and anomaly detection. You’ll practice identifying malicious patterns like DDoS attacks, port scanning, and data exfiltration attempts. Case studies simulate actual security incidents, teaching you to correlate evidence and trace attack vectors through network traffic.<br /><br />A key focus is developing actionable reporting skills for incident response teams. You’ll learn to document findings, create visualizations of network activity, and present technical details to both security teams and non-technical stakeholders. The course culminates in a capstone project where you analyze a complex traffic capture and produce a professional security assessment report.<br /><br />Designed for aspiring cybersecurity analysts, network administrators, and IT professionals, this training bridges the gap between theoretical knowledge and practical traffic analysis skills. You’ll finish with hands-on experience using industry-standard tools and techniques that are immediately applicable in security operations centers and forensic investigations.<br /><br /></div><h1>Course Syllabus</h1><div><strong>Module 1: Network Forensics and Network Traffic Analysis</strong></div><div><strong>Module 2: Packet Capture and Filtering with Wireshark</strong></div><div><strong>Module 3: DPI and Network Protocol Analysis</strong></div><div><strong>Module 4: Advanced Traffic Analysis and Reporting</strong></div><div><strong>Module 5: Final Project and Course Wrap-Up</strong></div><div><br /></div><h1><strong>Course objectives</strong></h1><ul><li>Describe the principles, objectives, tools, and challenges of network forensics and traffic analysis</li><li>Apply packet capture and filtering techniques using tools like Wireshark</li><li>Analyze network protocols and traffic using Deep Packet Inspection (DPI) and flow data to identify potential security threats</li><li>Create detailed reports from captured data tailored for stakeholders and incident response teams</li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>To get the most out of this course, you should have:<br /><br /></div><div><strong>Basic networking knowledge</strong> (TCP/IP, OSI model fundamentals)</div><div><strong>Cybersecurity fundamentals<br />Familiarity with command-line interfaces</strong> (Linux/Windows)<br /><br /></div><div>If you need to build your knowledge in these areas before starting this course, the following resources can help:<br /><a href="https://www.coursera.org/learn/information-technology-it-fundamentals-for-everyone">Information Technology (IT) Fundamentals for Everyone</a><br /><a href="https://www.coursera.org/specializations/cybersecurity-fundamentals">Cybersecurity Fundamentals Specialization</a></div>
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Network Traffic Analysis with Wireshark

This comprehensive course teaches you network traffic analysis and its critical applications in cybersecurity and ethical hacking. Using Wireshark, the industry-standard packet analysis tool, you'll develop essential skills for detecting threats, investigating security incidents, and strengthening network defenses through hands-on, real-world scenarios.You'll begin by exploring the fundamentals of network forensics and traffic analysis. You'll learn to distinguish between different investigation approaches while understanding the ethical and legal considerations involved.

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  <div>The user interface (UI) design market is growing fast, driven by the increasing demand for digital products that prioritize user experience (UX) (Global Growth Insights).  <br /><br />This course offers an in-depth exploration of UX/UI fundamentals, design thinking, user-centered methodologies, interaction principles, and the strategic use of UX animations, enabling learners to craft seamless, visually engaging, and highly functional digital experiences. <br /><br />During the course, you’ll look at key aspects of UX/UI design, how to conduct heuristic evaluations to identify and fix usability issues and use user journey mapping to understand and optimize user flows. You’ll explore A/B testing techniques that help you make data-driven design decisions. Plus, you’ll dive into microinteraction design to create engaging user interactions and learn to build responsive user interfaces that ensures consistency across different devices.  <br /><br />Using industry-leading tools like Miro and Figma, you’ll apply your skills through hands-on labs, real-world design challenges, and usability testing frameworks. Then, you’ll complete a project designing a fully interactive UX/UI prototype for a health and fitness app, conduct usability testing, gather feedback, and refine your design to create a polished product, perfect for your portfolio!  <br /><br />If you’re keen to kickstart your UX/UI career with the core UI/UX skills employers are looking for, enroll today!</div>
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UI/UX Design: Usability and Visual Principles

Learn beginner-level UI/UX design principles that enhance usability and visual appeal in digital products. Master key skills like user research, wireframing, color theory, and typography to create intuitive interfaces. Translate user needs into effective design solutions with hands-on projects and real-world examples. Elevate your design proficiency by understanding fundamental concepts that improve user interaction and satisfaction.

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  <div>Welcome to the Practice Exam for CompTIA Tech+ Certification course. This reading will introduce the course outline and prerequisites, giving you a comprehensive overview of what to expect and the background knowledge required to navigate the course content successfully.<br /><br /></div><div><strong>Prerequisites<br /></strong><br /></div><div>This course assumes you’re familiar with the IT fundamentals that the CompTIA Tech+ exam tests on. <br /><br /></div><div><strong>Course Outline<br /></strong><br /></div><div><strong>Module 1:</strong> About the CompTIA Tech+ Certification Exam<br /><br /></div><ul><li>Lesson 1: Welcome to the Course<br />Lesson 2: CompTIA Tech+<br /><br /></li></ul><div><strong>Module 2:</strong> Practice Exam for Tech+ Certification<br /><br /></div><div><strong>Module 3:</strong> Course Wrap-Up and Cloud Basics Exam<br /><br /></div><ul><li>Lesson 1: Course Wrap-Up<br />Lesson 2: [Optional] Cloud Basics Exam<br /><br /></li></ul>
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Practice Exam for CompTIA Data+ Certification

CompTIA Tech+ certification is highly regarded by employers and can open the door to entry-level roles such as help desk technician or junior system administrator. This short course helps you prepare effectively for the certification exam.  

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  <div>Transformers have revolutionized the way machines understand and process information, not only in text but also in <strong>images</strong>. Vision Transformers (ViTs) have become a cornerstone in modern computer vision, outperforming traditional convolutional networks on many tasks. However, fine-tuning such powerful models can be <strong>computationally expensive</strong> and <strong>memory-intensive</strong>, especially on personal hardware or CPUs.<br /><br /></div><div>That’s where <strong>LoRA (Low-Rank Adaptation)</strong> comes in. LoRA makes it possible to <strong>adapt large pretrained models efficiently</strong>. This project demonstrates how you can take a <strong>pretrained Vision Transformer</strong> and fine-tune it to recognize <strong>new image classes</strong> even with <strong>limited data and no GPU</strong>. By completing this project, you’ll not only understand how modern vision models work but also how to adapt them <strong>practically and efficiently</strong> for your own use cases.<br /><br /></div><h1>A Look at the Project Ahead</h1><div><strong>In this project, you will:</strong></div><ul><li><strong>Learn the architecture of Vision Transformers (ViTs): </strong>how images are processed and interpreted.</li><li><strong>Explore the intuition behind LoRA (Low-Rank Adaptation): </strong>Why is it so effective for lightweight fine-tuning.</li><li><strong>Load and modify a pretrained DeiT (Data-efficient Image Transformer)</strong> model from Hugging Face.</li><li><strong>Extend a model’s classifier head</strong> to recognize new labels and correctly map label IDs for training.</li><li><strong>Freeze base model weights</strong> and <strong>insert LoRA adapters</strong> to adapt only selected attention layers.</li><li><strong>Preprocess image datasets</strong> using feature extractors compatible with vision transformers.</li><li><strong>Fine-tune and evaluate</strong> the adapted model on a small image dataset.</li></ul><div><strong>Skills You’ll Gain:</strong></div><ul><li>Proficient understanding of <strong>transfer learning</strong> and <strong>parameter-efficient fine-tuning (PEFT)</strong>.</li><li>Practical experience with <strong>Hugging Face Transformers</strong>, <strong>PEFT</strong>, and <strong>Datasets</strong> libraries.</li><li>Hands-on implementation of <strong>LoRA adapters</strong> in PyTorch.</li></ul><div><br /></div><h1>What You'll Need</h1><ul><li>A compatible modern browser (Chrome, Firefox, Edge, or Safari).</li><li>Basic Python and PyTorch programming</li><li>High-level understanding of transformer architecture.</li><li>Introductory  knowledge of linear algebra (specifically matrix manipulation).</li><li>Curiosity about transformers and modern computer vision methods</li></ul>
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Fine Tune a Vision Model for Image Classification with LoRA

Learn to fine-tune a Vision Transformer (ViT) with LoRA (Low-Rank Adapters) for targeted image classification and adapt AI models to your own data for any use case. You might think fine-tuning a model with millions of parameters requires massive compute and memory, not with LoRA. In this guided project, you'll load a pretrained DeiT model using Hugging Face's API, extend its ability to detect new classes, and train it on a custom dataset. You'll learn how parameter-efficient fine-tuning (PEFT) makes adapting large transformer models fast, lightweight, and cost-effective for any use case.

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  <div> Have you ever wished you could redesign your favorite outfit with just a few words: <em>"make the sleeves longer," "turn this dress into silk," or "change the color to emerald green"</em>? <br /> <br />Fashion design is a deeply creative process, but traditional editing tools demand time, precision, and technical skill. What if AI could understand your description, automatically identify the clothing item you want to change, and instantly generate your new design?  In this project, you’ll build an <strong>AI-powered fashion design assistant</strong> that combines the <strong>CLIPSeg segmentation and</strong> <strong>Segment Anything Model (SAM)</strong> with <strong>DALL·E 2</strong> to make text-guided fashion editing effortless. Together, these models form a seamless workflow: SAM figures out <em>what</em> to edit, and DALL·E 2 decides <em>how</em> to recreate it. You’ll integrate both into a user-friendly <strong>Gradio web app</strong>, where users can upload an image, select a fashion item, describe the desired transformation, and see the result generated in seconds. Along the way, you’ll learn how to bridge computer vision and generative AI to build real-world creative tools.  <br /><br /></div><h1>What You’ll Learn</h1><div>By the end of this project, you will be able to: </div><ul><li><strong>Apply the CLIPSeg and Segment Anything Model for automatic mask generation:</strong> Use SAM to detect and segment fashion elements from uploaded images, enabling precise and flexible edits without manual input.</li><li><strong>Leverage DALL·E 2 for text-guided inpainting:</strong> Use OpenAI’s generative model to redesign masked regions according to user prompts, changing textures, colors, and styles seamlessly.</li><li><strong>Build an interactive Gradio web interface:</strong> Create a shareable AI design assistant where users can visualize edits, experiment with different looks, and generate new outfit ideas from plain language.</li><li><strong>Combine vision and generative AI workflows:</strong> Understand how to connect segmentation models with diffusion-based image generators for practical, creative applications.</li></ul><div><br /></div><h1>Who Should Enroll</h1><ul><li><strong>AI enthusiasts and developers</strong> interested in combining computer vision and generative models.</li><li><strong>Designers, artists, or fashion students</strong> curious about how AI can accelerate and inspire the design process.</li><li><strong>Students and researchers</strong> who want hands-on experience building multi-model AI systems with real-world creative use cases.</li></ul><div><br /></div><h1>Why Enroll</h1><div>This project brings the worlds of <strong>fashion, creativity, and artificial intelligence</strong> together. Instead of manually editing images or spending hours rendering new designs, you’ll learn how to build a tool that understands your intent through text, transforming design into an instant, interactive experience. By the end, you’ll have a <strong>working AI fashion design assistant</strong>, a deep understanding of <strong>SAM and DALL·E 2 integration</strong>, and practical experience deploying an <strong>AI-powered web app</strong> that turns imagination into visual reality.</div><div><br /></div><h1>What You'll Need</h1><div>To get the most out of this project, you should have:</div><ul><li>Basic Python programming skills.</li><li>Some familiarity with computer vision or generative AI concepts (helpful but not required).</li><li>Curiosity about how AI can revolutionize creative design workflows.</li></ul><div>All dependencies are pre-configured in the environment, and the project runs best on the latest versions of Chrome, Edge, Firefox, or Safari.</div>
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Build a Fashion Design Web App with DALL-E, Gradio and SAM

Build an AI fashion assistant with CLIPSeg, SAM, and DALL·E 2 that lets users virtually try on, restyle, and redesign clothes through text prompts. Use CLIPSeg and SAM to detect and segment clothing items or accessories in any image, then integrate DALL·E 2 to inpaint and redesign those regions from simple text prompts. Experiment with new fabrics, styles, and colors, all generated intelligently and seamlessly. By the end, you’ll have created a shareable web app that empowers anyone to visualize and prototype fashion ideas instantly through the power of computer vision and generative AI.

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  <div>Ready to put your ethical hacking skills to the test and power your cybersecurity career forward?<br /><br />After completing this capstone project, you will be able to:</div><ul><li>Demonstrate advanced ethical hacking and defensive cybersecurity skills through realistic Red Team and Blue Team exercises.</li><li>Conduct reconnaissance, exploitation, privilege escalation, and persistence techniques using professional penetration testing tools like Metasploit and Kali Linux.</li><li>Analyze system vulnerabilities, logs, and Indicators of Compromise to detect and respond to simulated attacks.</li><li>Integrate OpenVAS and the MITRE ATT&amp;CK framework to evaluate and improve security posture in defensive operations.</li><li>Apply generative AI tools to enhance reconnaissance, exploit development, and anomaly detection in cybersecurity workflows.</li><li>Produce a professional-grade incident response report, documenting findings, mitigations, and lessons learned from simulated attacks.</li></ul><div>This hands-on capstone course is designed for cybersecurity analysts, ethical hackers, and IT professionals who want to showcase their real-world expertise in offensive and defensive security operations.<br /><br /></div><div>You’ll begin on the Red Team, using industry-standard tools like Kali Linux, Nmap, and Metasploit to conduct reconnaissance, exploitation, privilege escalation, and persistence. After that, you’ll switch to the Blue Team, performing log analysis, detecting Indicators of Compromise (IOCs), and executing defensive responses using OpenVAS and the MITRE ATT&amp;CK framework.<br /><br /></div><div>You’ll also explore the transformative role of Generative AI in cybersecurity, applying AI-assisted reconnaissance, exploit generation, and anomaly detection to enhance both offensive and defensive strategies.<br /><br /></div><div>Each lab challenges you to prove your mastery through practical exercises, culminating in a final project that demonstrates your ability to investigate, defend, and report on simulated cybersecurity incidents.<br /><br /></div><div>If you’re ready to validate your cybersecurity and ethical hacking skills with a portfolio-ready capstone project, enroll today!<br /><br /></div><div><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>The following skills are required to be successful with this course: </div><ul><li>We recommend completing all previous courses in the <strong>IBM Ethical Hacking with OS Tools Professional Certificate</strong> before starting this capstone.</li></ul><div><br /></div><h1>Course Syllabus</h1><div><strong>Module 1: Initial Access </strong></div><ul><li>Lesson 0: Welcome</li><li>Lesson 1: Network Reconnaissance and Exploitation </li><li>Lesson 2: Initial Access: Checklist and Graded Quiz</li></ul><div><strong>Module 2: Privilege Escalation </strong></div><ul><li>Lesson 1: Privilege Escalation and Post-Exploitation Techniques</li><li>Lesson 2: Privilege Escalation: Checklist and Graded Quiz </li></ul><div><strong>Module 3: Persistence Techniques </strong></div><ul><li>Lesson 1: Persistence Techniques and Evasion </li><li>Lesson 2: Persistence Techniques: Checklist and Graded Quiz</li></ul><div><strong>Module 4: Detection and Response </strong></div><ul><li>Lesson 1: Threat Detection and Response</li><li>Lesson 2: Detection and Response: Checklist and Graded Quiz</li></ul><div><strong>Module 5: Generative AI in Cybersecurity </strong></div><ul><li>Lesson 1: Generative AI in Offensive Cybersecurity</li><li>Lesson 2: Generative AI in Defensive Cybersecurity</li><li>Lesson 3: Checklist and Graded Quiz</li></ul><div><strong>Module 6: Reporting, Recovery, Final Project, and Course Wrap-Up </strong></div><ul><li>Lesson 1: Writing, Compiling, and Submitting Your Incident Report </li><li>Lesson 2: Final Assessment</li><li>Lesson 3: Course Wrap-Up</li></ul>
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Capstone Project: Ethical Hacking with Gen AI

Master red and blue team cybersecurity skills with hands-on labs in ethical hacking, defense, and AI-assisted threat detection. Simulate real-world cyberattacks and responses to boost your career in just 6 weeks.

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  <div>Build job-ready cybersecurity skills through practical experience in vulnerability assessment and incident response using OpenVAS.<br /><br />After completing this course, you will be able to:</div><ul><li>Install and configure OpenVAS in a Linux environment, setting up scan targets and running initial vulnerability assessments.</li><li>Interpret and analyze OpenVAS scan results, applying CVSS scoring to assess and prioritize vulnerabilities.</li><li>Evaluate risks based on asset value, exploitability, and impact to develop effective remediation plans.</li><li>Detect and contain active threats by correlating system logs, indicators of compromise, and OpenVAS results.</li><li>Produce structured, professional post-incident reports integrating threat intelligence and policy recommendations.</li><li>Simulate a complete incident response lifecycle using OpenVAS, from detection and containment to remediation and reporting.</li></ul><div>Designed for cybersecurity professionals and aspiring ethical hackers, this 4-week course immerses you in realistic scenarios where you’ll install and configure OpenVAS, run vulnerability scans, interpret CVSS scores, and identify security gaps across networked systems.<br /><br /></div><div>You’ll analyze system and network logs, correlate scan results with attack frameworks like MITRE ATT&amp;CK, and apply containment strategies to mitigate active threats. Moving beyond detection, you’ll learn to prioritize risks, plan effective remediation, and document findings in structured, evidence-based reports.<br /><br /></div><div>The course concludes with a full incident response simulation where you’ll apply OpenVAS to detect, contain, and remediate an attack scenario while producing a professional-grade incident report.<br /><br /></div><div>Through guided labs, assignments, and a final project, you’ll gain real-world skills that strengthens your cybersecurity portfolio and prepares you for roles in vulnerability management and incident response.<br /><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>Working knowledge of networking, operating systems (especially Linux), and cybersecurity concepts is helpful, but you don’t need to be an expert to get started.<br /><br /></div><h1>Course Syllabus</h1><div><strong>Module 1: Installation and Configuration of OpenVAS</strong></div><ul><li>Module Summary and Learning Objectives</li><li>Understanding Vulnerability Assessment and its Role in Defense </li><li>What is OpenVAS and How Does it Work</li><li>GVM Core Concepts and Terminology</li><li>OpenVAS Architecture and Components</li><li>Comparison: OpenVAS vs Nessus vs Qualys</li><li>Step-by-Step OpenVAS Setup Explained</li><li>Feed Synchronization, GVM Services and Startup Logic</li><li>Navigating the OpenVAS Dashboard: A Guided Tour</li><li>GVM Overview</li><li>Configuring Scan Targets and Credentials in OpenVAS</li><li>Understanding GVM Daemon Dependencies</li></ul><div><strong>Module 2: Vulnerability Analysis and Remediation Planning</strong></div><ul><li>Module Summary and Learning Objectives</li><li>Anatomy of an OpenVAS Report: Key Metrics and Tags</li><li>Common Vulnerabilities Identified by OpenVAS</li><li>CVSS in Practice: Interpreting Real-World Scores</li><li>CVSS Scoring Explained</li><li>Vulnerability Lifecycle Management</li><li>Ranking Risk: Asset Context, CVSS, and Exploitability</li><li>Patching vs Mitigation: Choosing the Right Response</li><li>Mitigation Planning for Enterprise Environments</li><li>Using Asset Value and Threat Intelligence to Prioritize Fixes</li></ul><div><strong>Module 3: Threat Detection and Incident Containment</strong></div><ul><li>Module Summary and Learning Objectives</li><li>How Vulnerability Data Supports Threat Hunting</li><li>OpenVAS Alerts and Real-Time Threat Intelligence</li><li>Correlating Logs and Scans to Identify Anomalies</li><li>Network Behavior and Anomalies</li><li>Mapping Scan Results to MITRE ATT&amp;CK Tactics</li><li>Containment Strategies</li><li>Response Strategy: When and How to Isolate a Host  </li><li>When to Disconnect or Reimage a Host</li><li>Key Tools and Techniques for Threat Containment </li></ul><div><strong>Module 4: Post-Incident Analysis, Final Project, Final Exam, and Course Wrap-Up</strong></div><ul><li>Module Summary and Learning Objectives</li><li>Writing and Structuring a Post-Incident Report</li><li>Integrating Threat Intelligence into After-Action Reports</li><li>Turning Lessons Learned into Policy Improvements</li><li>Best Practices for Report Writing</li><li>Final Project </li><li>Final Graded Quiz</li><li>Course Wrap-Up and Next Steps </li></ul>
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OpenVAS for Vulnerability Assessment and Incident Response

Learn OpenVAS for vulnerability assessment and incident response. Install, configure, and use OpenVAS to detect, prioritize, and remediate cybersecurity threats in real-world simulations.

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  <div>Metasploit is one of the most widely used cybersecurity frameworks for ethical hacking and penetration testing. This course builds your job-ready skills in exploitation techniques and attack simulation using Metasploit—skills employers actively seek.<br /><br />After completing this course, you will be able to:</div><ul><li>Explain the structure, components, and functionalities of the Metasploit Framework and its role in penetration testing.</li><li>Configure and operate Metasploit to perform reconnaissance, exploitation, and post-exploitation tasks in controlled environments.</li><li>Develop and modify exploits using auxiliary modules and fuzzing techniques to identify vulnerabilities in systems and applications.</li><li>Simulate real-world attack scenarios, including privilege escalation, persistence, and social engineering tactics.</li><li>Analyze and document penetration test results to produce actionable security recommendations and mitigation strategies.</li><li>Integrate Metasploit into ethical hacking workflows to enhance red teaming and vulnerability assessment processes.</li></ul><div>You’ll start by learning how to install and configure the Metasploit Framework, exploring its architecture, payloads, and session management. You’ll then simulate exploits and apply auxiliary modules to identify vulnerabilities, modify attacks, and perform fuzz testing. <br /><br /></div><div>You will move into advanced post-exploitation techniques, including privilege escalation, persistence, and evasion tactics. The course concludes with a simulated penetration test where you’ll apply your skills to conduct a full assessment and generate a professional report.<br /><br /></div><div>By the end of this course, you’ll be able to execute sophisticated exploits, automate penetration testing workflows, and understand how attackers compromise systems, preparing you to strengthen defenses and pursue advanced cybersecurity roles.<br /><br /></div><div>If you want hands-on penetration testing experience you can showcase on your resume, enroll today!</div><div><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>Working knowledge of networking, operating systems (especially Linux), and cybersecurity concepts is helpful, but you don’t need to be an expert to get started. <br /><br /></div><h1>Course Syllabus</h1><div><strong>Welcome to this course</strong></div><ul><li>Welcome</li></ul><div><strong>Module 1: Metasploit Setup and Core Exploitation Techniques </strong></div><ul><li>Module Summary and Learning Objectives</li><li>Introduction to Metasploit and Exploitation Basics</li><li>Exploring the Metasploit Framework</li><li>Glossary, Cheat Sheet, and Graded Quiz </li></ul><div><strong>Module 2: Exploit Development and Auxiliary Module Usage</strong></div><ul><li>Module Summary and Learning Objectives</li><li>Exploring the Metasploit Auxiliary Modules</li><li>Exploit Development and Advanced Techniques</li><li>Glossary, Cheat Sheet, and Graded Quiz </li></ul><div><strong>Module 3: Post-Exploitation and Attack Simulations </strong></div><ul><li>Module Summary and Learning Objectives</li><li>Privilege Escalation and Maintaining Access </li><li>Persistence and Evasion</li><li>Simulated Network Attacks</li><li>Glossary, Cheat Sheet, and Graded Quiz </li></ul><div><strong>Module 4: Web and Social Engineering Attacks </strong></div><ul><li>Module Summary and Learning Objectives</li><li>Web Application Exploitation </li><li>Phishing and Credential Harvesting </li><li>USB and Physical Social Engineering Attacks </li><li>Glossary, Cheat Sheet, and Graded Quiz </li></ul><div><strong>Module 5: Final Project: Simulated Penetration Test Using Metasploit</strong></div><ul><li>Final Project </li><li>Course Wrap-Up</li></ul><div><br /></div><div><br /></div>
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Metasploit for Exploitation and Pen Testing

Learn Metasploit for ethical hacking and penetration testing. Gain hands-on skills in exploitation, post-exploitation, and attack simulations to identify and defend against cybersecurity threats in just a few hours.

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  <div>Kali Linux is the go-to operating system for ethical hackers, penetration testers, and cybersecurity professionals. This course combines practical hands-on learning with foundational knowledge to help you confidently use Kali Linux for security testing.<br /><br />After completing this module, you will be able to:</div><ul><li>Apply job-ready skills to perform ethical hacking and penetration testing using Kali Linux tools.</li><li>Configure and install Kali Linux in virtualized environments for secure and efficient testing setups.</li><li>Utilize Kali Linux tools to investigate, analyze, and exploit vulnerabilities in simulated environments.</li><li>Develop Python and Bash scripts to automate system maintenance, security, and penetration workflows.</li><li>Customize and optimize Kali Linux tools to enhance performance and adapt them to specific security needs.</li><li>Integrate scripting, automation, and tool configuration to design efficient solutions for real-world cybersecurity challenges.</li></ul><div><br />You’ll begin by setting up and configuring Kali Linux, securing your environment, and exploring its architecture. You’ll dive into powerful cybersecurity tools for reconnaissance, forensics, and exploitation, gaining hands-on experience in real-world testing scenarios. <br /><br /></div><div>Then you will learn automation using Bash and Python scripting, customizing and streamlining tasks to enhance efficiency and precision. Finally, you’ll apply your skills in a project that challenges you to automate a security workflow and demonstrate your mastery of Kali Linux.<br /><br /></div><div>If you’re ready to elevate your cybersecurity career with the power of Kali Linux, enroll today!</div><div><br /></div><div><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>The following skills are required to be successful with this course: </div><ul><li> Foundational cybersecurity knowledge.</li></ul><div><br /></div><h1>Course Syllabus</h1><div><strong>Module 1: Overview of Kali Linux</strong></div><ul><li>Exploring Kali Linux</li><li>Hardening Kali Linux</li></ul><div><strong>Module 2: Kali Linux Tools</strong></div><ul><li>Kali Linux Tools for Investigation</li><li>Kali Linux Tools for Exploitation and Penetration Testing</li></ul><div><strong>Module 3: Scripting and Automation</strong></div><ul><li>Scripting with Python</li><li>Scripting with Bash</li></ul><div><strong>Module 4: Final Project and Course Wrap-Up</strong></div><ul><li>Final Project</li><li>Course Wrap-Up</li></ul><div><strong>Module 5 (Optional): Linux Overview</strong></div><ul><li>Linux Operating System</li><li>Navigating the Linux Operating System</li></ul><h1><br /></h1>
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Kali Linux for Ethical Hacking

Learn Kali Linux for ethical hacking and cybersecurity. Master installation, configuration, scripting, and automation with Kali Linux tools for penetration testing and vulnerability assessment in just a few hours.

<div>Get ready to build the generative AI (GenAI) skills employers need and future-proof your UI/UX career!&nbsp;<br><br></div><div>Recruiters are actively looking for design professionals who can combine creativity and expert design thinking with AI fluency. This IBM specialization gives you the practical skills and understanding you need to work with popular AI tools confidently and enhance every stage of the UI and UX design process.&nbsp; From ideation to prototyping and delivering innovative user experiences, you can leverage GenAI to work faster and smarter.&nbsp;<br><br></div><div>During this specialization, you will:&nbsp;</div><ul><li>Build the job-ready skills to design innovative UI/UX solutions, and create dynamic, personalized user experiences and interfaces (UX UI), powered by GenAI&nbsp;</li><li>Master the fundamentals of generative AI and explore real-world applications across text, images, audio, video, design, and data&nbsp;</li><li>Apply prompt engineering techniques to craft precise prompts and generate creative, usable design outputs&nbsp;</li><li>Learn to use a variety of AI tools, including ChatGPT, Google Gemini, IBM WatsonX, Miro, Figma, Uizard, Visily, and UXPilot, for faster ideation, automated prototyping, design personalization, and streamlined user testing&nbsp;</li></ul><div>&nbsp;</div><div><strong>Program overview</strong>&nbsp;</div><div>During this program, you’ll learn how to use generative AI to ideate faster, automate repetitive design tasks, and produce innovative, user-centered solutions. Through guided projects and real-world design challenges, you’ll explore how AI can assist in user research, journey mapping, prototyping, and usability testing. Plus, you’ll gain valuable hands-on experience using leading tools such as ChatGPT, Google Gemini, IBM WatsonX, Miro, Figma, Uizard, Visily, and UXPilot to generate ideas, create design assets, and evaluate user feedback effectively.&nbsp;</div><div>&nbsp;</div><div>As you progress, you’ll also gain experience in prompt engineering, multimodal design generation, and conversational UX. And you’ll learn how to apply human-centered design principles to ensure that AI-powered interfaces remain accessible, ethical, and inclusive.&nbsp;</div><div>&nbsp;</div><div>The program then culminates in a project where you’ll apply your new skills to design a complete, AI-enhanced user experience, demonstrating your ability to merge design creativity with emerging technologies. By the end, you will have the skills to bring generative AI into your UI/UX design workflow, create dynamic and adaptive interfaces, and stay ahead in the evolving world of digital product design.&nbsp;</div><div>&nbsp;</div><div><strong>Job outlook</strong>&nbsp;</div><div>The demand for UI/UX professionals who can integrate generative AI into design and creative processes is rapidly increasing across industries. Organizations are seeking designers and innovators who can use AI tools to improve efficiency, personalization, and user engagement.&nbsp;</div><ul><li>&nbsp;</li><li>LinkedIn’s Global Talent Report identifies AI fluency and UX design among the top in-demand skills for digital professionals worldwide.&nbsp;</li><li>Glassdoor reports that UI and UX designers in the United States earn an average annual salary of over $90,000, with rising demand for professionals skilled in AI-powered design tools and workflows.&nbsp;</li></ul><div><br><strong>Prerequisites</strong>&nbsp;</div><div>This specialization is ideal for professionals who already have basic UI/UX knowledge and design tool skills. No AI experience needed, and it is suited to diverse backgrounds.&nbsp;</div>

Generative AI for UI/UX Design Specialization 

Future-proof your UI/UX career with GenAI skills that power human-centered, data-driven UI/UX experiences. Plus, get hands-on experience using popular AI tools and techniques. 

<div>Ready to build the hands-on skills that turn ideas into intuitive, accessible, and visually engaging experiences? Want to become the go-to UI/UX designer businesses are looking for?&nbsp;</div><div>This beginner-friendly IBM Professional Certificate is specifically designed for aspiring designers, career changers, and existing creative professionals who want to build the complete set of UI/UX skills to research, design, and prototype user-centred digital experiences. No prior experience required.&nbsp;</div><div>&nbsp;</div><div>After completing this Professional Certificate, you will be able to:&nbsp;</div><ul><li>Conduct user research and design paper prototypes, wireframes, and interactive mockups using Figma&nbsp;</li><li>Design adaptive and responsive layouts for mobile, tablet, and desktop screens that deliver seamless user experiences&nbsp;</li><li>Apply usability testing, design thinking, and microinteraction techniques to enhance accessibility and engagement&nbsp;</li><li>Build a professional design portfolio that showcases your ability to create user-focused digital products&nbsp;</li></ul><div>&nbsp;</div><div><strong>Program overview</strong>&nbsp;</div><div>&nbsp;</div><div>UI/ UX designers play a critical role in shaping how people experience technology. They combine creativity, research, and technical understanding to make digital products simple, effective, and enjoyable to use. &nbsp;</div><div>During this program, you’ll explore every step of the design process, including user research, journey mapping, wireframing, and usability testing. You’ll gain practical experience designing paper prototypes, interactive mockups, and high-fidelity prototypes in Figma. Plus, you’ll learn how to design adaptive layouts that look and feel consistent across different screen types.&nbsp;</div><div>&nbsp;</div><div>As you learn, you’ll get hands-on in projects and realistic design challenges that mirror what professionals do in the workplace. You’ll create user personas, test design ideas, and refine your interfaces based on user feedback. You’ll also master topics such as accessibility, information architecture, and interaction design, learning how to use microinteractions to make designs more intuitive and engaging.&nbsp;</div><div>&nbsp;</div><div>You will work through a comprehensive capstone project that ties all your skills together. In this project, you’ll design a complete, user-centered experience from concept to prototype and compile your work into a professional portfolio. &nbsp;</div><div>&nbsp;</div><div>If you’re keen to earn an IBM Professional Certificate that confirms your job-ready UI/UX skills, enhances your resume, and demonstrates your practical readiness for roles such as UX Designer, UI Designer, Product Designer, or Interaction Designer, enroll today! <br><br><strong>Job outlook</strong>&nbsp;</div><div>Professionals with UI and UX design skills are in high demand around the world:&nbsp;</div><ul><li>Employment for web and digital interface designers is expected to grow 7% till 2034 (U.S. Bureau of Labor Statistics)&nbsp;</li><li>The global UX services market is forecast to grow to 54.9 billion dollars by 2032 (Fortune Business Insights)&nbsp;</li><li>More than 100,000 open positions worldwide list UI or UX design as a key skill across industries, including technology, e-commerce, healthcare, and finance (LinkedIn Jobs)&nbsp;</li></ul><div><br><strong>Prerequisites</strong>&nbsp;</div><div>This is a beginner-level program designed for anyone interested in starting or advancing a career in design. No previous experience in design, coding, or software tools is required. Curiosity, creativity, and a willingness to learn are the only prerequisites.&nbsp;</div>

IBM UI/UX Designer Professional Certificate 

Master the UX/UI skills businesses need to design engaging, functional, and accessible digital designs that help them stand out and grow in today’s competitive world. 

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  <div>Creating beautiful poetry no longer needs to be daunting. This AI Poetry Studio is a <strong>multilingual</strong>, <strong>multi-agent</strong> system that transforms your poem’s theme and preferences into a fully crafted, polished piece—<strong>in any language you choose</strong>.</div><div>The journey begins with a single input from you: the theme of your poem, plus optional details like language, poetic form, and tone. From there, a team of specialized AI agents takes over, each with a distinct role in the creative process:</div><ul><li><strong>Author Agent</strong> captures your theme and preferences in one clear brief to kick off the session.</li><li><strong>Muse Agent</strong> proposes three distinct poetic directions—titles, moods, and concepts—and selects the strongest one to pursue.</li><li><strong>Verse Agent</strong> develops the selected idea into a complete poem with crisp line breaks and imagery tailored to your style.</li><li><strong>Editor Agent</strong> lightly polishes for rhythm and clarity while preserving meaning, then signals completion with POEM_COMPLETE.</li></ul><div>Working together via the AutoGen framework and powered by the GPT-5 nano model, these AI agents collaboratively create poetry that is <strong>well-structured and elegant</strong>, delivered in your chosen language. By the end of the session, you receive a beautifully finished poem that showcases the seamless orchestration of multiple intelligent agents blending creativity with precision.</div><div><br />This system empowers you to co-create original poetry with AI, experiencing the harmony between your input and advanced multi-agent workflows.<br /><br /></div><h1>Who Should Enroll</h1><ul><li>Developers, data scientists, and AI enthusiasts interested in building creative applications with multi-agent systems</li><li>Writers, educators, and poetry lovers curious about how AI can assist in narrative generation and content design</li><li>Learners eager to explore practical applications of <strong>AutoGen (AG2) &amp;</strong> <strong>OpenAI’s GPT-5 </strong>in collaborative creative workflows.</li></ul><div><br /></div><h1>A Look at the Project Ahead</h1><div>After completing this guided project, you will be able to do:</div><ul><li><strong>Understand AutoGen’s multi-agent architecture</strong> – Learn how to design and coordinate specialized agents to simulate a real-world poetry creation workflow.</li><li><strong>Build intelligent writing agents</strong> – Create agents with defined roles such as Muse Generator, Verse Crafter, and Poetry Editor to handle different stages of poetic composition.</li><li><strong>Orchestrate collaborative book creation</strong> – Combine individual agent contributions to generate cohesive poems with consistent rhythm, tone, and literary quality.</li><li><strong>Learn to build and run agent workflows with AutoGen</strong> – Use Python and AutoGen to define agents, manage communication, and streamline task execution.</li><li><strong>Deliver personalized poetic experiences</strong> – Produce customized poems based on user inputs like theme, mood, or phrase.</li></ul><h1>What You'll Need</h1><div><strong>Technical Requirements:</strong> A basic understanding of Python programming. Experience with AI or LLMs is helpful but not required.</div><div><strong>Browser Setup:</strong> A modern web browser to access CrewAI tools, follow the guided environment, and run your code.</div>
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Multilingual AI Agents for Poetry using GPT-5 & AutoGen/AG2

Learn how to use AutoGen’s multi-agent framework with GPT-5 to collaboratively generate and refine poetry. The agents are multilingual—generate poems in any language you choose. You provide the theme (plus optional form and tone); a Muse proposes and selects the concept, a Verse Crafter writes the poem, and a Poetry Editor lightly polishes and signals POEM_COMPLETE. A simple, guided flow that shows how GPT-5 + AutoGen turn your idea into a finished poem—without the heavy technical setup. Learn AutoGen's multi-agent architecture by taking this hands-on project now!

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  <div>Choosing the perfect outfit can often feel overwhelming—but not anymore. AutoGen Wardrobe Wizard is a multi-agent AI system designed to help you effortlessly plan stylish looks tailored to your wardrobe, personal style, and the occasion.</div><div><br />The process begins with <strong>Meta's Llama vision model</strong>, which analyses your uploaded photo and extracts clothing items to build a digital wardrobe—laying the groundwork for personalised recommendations.</div><div>Powered by <strong>Microsoft’s AutoGen</strong> framework, the system brings together a team of specialised AI agents that function like your own virtual styling team:</div><ul><li><strong>Color Analyst</strong> picks colors that complement your skin tone</li><li><strong>Style Planner</strong> recommends outfit types based on your preferences and the occasion</li><li><strong>Silhouette Analyst</strong> suggests flattering cuts and designs</li><li><strong>Outfit Board Creator</strong> assembles everything into a cohesive final look</li></ul><div>To complete the experience, <strong>OpenAI’s DALL·E</strong> generates a photorealistic outfit board, presenting your styled ensemble in a magazine-inspired flat lay.</div><div><br />By the end of this project, you'll have created a smart fashion assistant that combines visual recognition, collaborative AI, and creative design. It's a fun, real-world example of how multi-agent systems can solve everyday problems—with intelligence and style.</div><div><br /></div><h1>A Look at the Project Ahead</h1><div>After completing this guided project, you will be able to do:</div><ul><li><strong>Understand AutoGen’s multi-agent architecture</strong> – Learn how to define and coordinate specialized agents to simulate real-world fashion consulting workflows.</li><li><strong>Build intelligent fashion agents</strong> – Create agents with defined roles such as color analysis, style planning, and style advising.</li><li><strong>Orchestrate collaborative outfit generation</strong> – Combine individual agent insights to produce a cohesive final outfit recommendation.</li><li><strong>Learn to build and run agent workflows with AutoGen</strong> – Use Python and AutoGen to define agents, manage communication, and control task execution.</li><li><strong>Generate visual outputs with DALL·E </strong>– Transform final outfit descriptions into AI-generated fashion boards using prompt-based image generation.</li><li><strong>Deliver personalized styling experiences </strong>– Generate tailored fashion suggestions based on user profile inputs like skin tone, occasion, and preferences.</li></ul><h1>What You'll Need</h1><div><strong>Technical Requirements:</strong> A basic understanding of Python programming. Familiarity with AI agents or large language models (LLMs) is helpful but not mandatory.</div><div><strong>Browser Setup:</strong> A modern web browser to access the AutoGen tools, follow the guided project environment, and run your code seamlessly.</div>
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AutoGen Wardrobe Wizard: A MultiAgent Fashion System

Learn how to combine Meta's Llama vision model, OpenAI’s DALL·E, and Microsoft’s AutoGen, a framework for orchestrating collaborative AI agents, to build an AI-powered virtual fashion stylist. This project uses modular agents such as a Color Analyst, Style Planner, Silhouette Analyst, and Outfit Board Creator to generate personalised outfit recommendations based on your uploaded photos. By the end, you’ll have a complete multi-agent system that blends computer vision, intelligent coordination, and creative design for real-world fashion use.

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  <div>AI agents are transforming how we interact with technology, automating tasks that once took hours into just a few clicks. By learning to build a Model Context Protocol (MCP) server and a LangGraph agent, you’ll gain hands-on experience in connecting AI models to real-world tools. This project is especially exciting because it shows how AI can manage everyday tasks, like email, using natural language—making technology smarter and more accessible. Completing this project will give you practical skills that can be applied to productivity tools, automation workflows, and modern AI applications.</div><div><br /><br /></div><h1>A Look at the Project Ahead</h1><div><strong>In this guided project, you will:</strong></div><ul><li>Build a <strong>Model Context Protocol (MCP) server</strong> to provide AI agents with structured access to tools and resources.</li><li>Develop a <strong>LangGraph ReAct agent</strong> that can send, search, and reply to Gmail messages using natural language commands.</li><li>Create a <strong>ReactJS frontend</strong> and <strong>Express backend</strong> to handle UI interactions and server communication.</li><li>Integrate <strong>Gmail OAuth authentication</strong> to securely manage user emails.</li></ul><div><strong>By the end of this lab you'll:</strong></div><ul><li>Understand what MCP server's are, how they work, and their typical use cases.</li><li>Build an AI agent that interact with external tools through MCP servers.</li><li>Design intelligent workflows using LangGraph agents.</li><li>Implement practical AI-powered automation in a web application.</li></ul><div><br /></div><h1>What You'll Need</h1><div><strong>Before starting this project, you should have</strong>:</div><ul><li>An interest in agentic AI and general understanding of how they work</li><li>A good understanding of how client-server HTTP interactions work</li><li>A good understanding of JavaScript and HTML/CSS in a web development context</li><li>Familiarity with the ReactJS framework and common hooks</li><li>Familiarity with Node environments and package management</li><li>A modern browser to run this CloudIDE environment (Chrome, Firefox, Safari, etc.)</li></ul>
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Build an MCP Server and LangGraph.js Agent to manage Gmail

Build a Model Context Protocol (MCP) server to enable smart Gmail actions in a LangGraph agent. This intermediate level project guides you through creating a smart email management system using OpenAI's GPT models and Anthropic's MCP. You’ll implement Gmail OAuth, design a React user interface with an Express backend, and build an agent that can manage emails with natural language prompts. By the end of the project, you'll be proficient with the MCP SDK and understand it's many practical use cases. Enroll now to learn this cutting-edge technology used across countless industries.

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  <div>Have you ever struggled to decide where to eat? With countless options and ever-changing food trends, choosing a restaurant can be overwhelming. In this project, you’ll build an <strong>AI-powered restaurant recommender system</strong> that goes far beyond simple filtering by cuisine or price. By orchestrating multiple <strong>CrewAI agents</strong> and integrating <strong>Retrieval-Augmented Generation (RAG)</strong> with a <strong>WatsonX Slate embedding model</strong> and <strong>Chroma vector database</strong>, you’ll create an intelligent dining assistant that learns from user history, interprets dining photos, and adapts to real-time food trends. This system doesn’t just suggest popular spots—it builds a nuanced profile of the user’s tastes and recommends restaurants that fit their preferences, budget, and style of dining.<br /><br /></div><h1>What You’ll Learn</h1><div>By the end of this project, you will be able to:</div><ul><li><strong>Build a RAG-powered retriever for restaurants</strong>: Encode restaurant data with WatsonX Slate embeddings and store them in Chroma for efficient similarity search.</li><li><strong>Create and coordinate multi-agent systems with CrewAI</strong>: Define agents like the <strong>Profile Builder</strong>, <strong>Coarse RAG Recommender</strong>, <strong>Food Trend Analyst</strong>, and <strong>Recommendation Finalizer</strong> to work collaboratively.</li><li><strong>Analyze multimodal user data</strong>: Use vision-capable LLMs to extract insights from dining photos and integrate them with textual reviews.</li><li><strong>Incorporate real-time web search</strong>: Use Serper to fetch trending cuisines and new restaurants, ensuring recommendations stay current.</li><li><strong>Deliver structured outputs with Pydantic</strong>: Standardize agent outputs for reliability and seamless downstream processing.</li><li><strong>Run and orchestrate an end-to-end pipeline</strong>: From user history to final ranked recommendations, you’ll deploy a fully functional AI restaurant recommender.</li><li><strong>Build a Gradio Web App for online chatbot!</strong></li></ul><div><br /></div><h1>Who Should Enroll</h1><ul><li><strong>Early-career data scientists and ML engineers</strong> who want hands-on experience building multi-agent systems.</li><li><strong>AI enthusiasts</strong> curious about CrewAI, RAG, and multimodal LLM applications.</li><li><strong>Students and researchers</strong> interested in applying cutting-edge AI to real-world recommendation challenges.</li><li><strong>Developers in food-tech or hospitality</strong> who want to explore AI-driven personalization.</li></ul><div><br /></div><h1>Why Enroll</h1><div>This project bridges <strong>recommender systems, multimodal AI, and agent orchestration</strong> to show how intelligent assistants can transform the way people discover restaurants. Instead of just listing top-rated spots, your system will <strong>understand the user’s preferences, stay updated with trends, and deliver personalized, trustworthy suggestions</strong>. By the end, you’ll have a working <strong>multi-agent CrewAI restaurant recommender</strong>, practical experience with <strong>RAG pipelines and multimodal LLMs</strong>, and insights into how AI can power the next generation of personalized dining experiences.</div><div><br /></div><h1>What You’ll Need</h1><div>To get the most out of this project, you should have:</div><ul><li>Basic Python programming knowledge.</li><li>Familiarity with LLM concepts and vector databases (helpful but not required).</li><li>(Optional) Sign up for a <strong>free</strong> Serper API account as a part of the project for real-time online search.</li><li>Interest in <strong>AI agents, recommender systems, and multimodal data</strong>.</li></ul><div>All dependencies are pre-configured in the environment, and the project runs best on current versions of Chrome, Edge, Firefox, or Safari.</div>
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Multi-Agent Restaurant Chatbot with Vision LLM, CrewAI & RAG

Build your own multi-agent restaurant recommendation chatbot with Multimodal LLM, CrewAI, Chroma DB and RAG. Learn to orchestrate agents through YAML-defined configurations and Pydantic schemas to ensure structured, reliable outputs. Use IBM watsonx slate embeddings and a Chroma vector database to power restaurant retrieval. Integrate a Vision LLM to analyze dining photos and a Serper web tool to capture real-time food trends. Explore how CrewAI enables collaborative agent workflows that combine RAG retrieval and online trend analysis to create an adaptive, smart dining assistant.

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  <div>Ready to leverage the power of generative AI (GenAI) to elevate your UI/UX design skills? This course equips designers and creative professionals with job-ready skills and hands-on experience integrating AI across every stage of the user experience workflow—from ideation and research to prototyping and conversational design. <br /><br /></div><div> </div><div> </div><div><strong><em>You will:</em></strong>  </div><ul><li>Design a portfolio-ready, AI-enhanced UX project that demonstrates your ability to integrate generative AI tools into modern design workflows. </li><li>Use UXPilot, Miro, Visily, Uizard, and Figma to streamline UX/UI processes and accelerate ideation, prototyping, and testing. </li><li>Leverage prompt-based AI to craft user personas, empathy maps, journey maps, and UX microcopy that reflect real user needs. </li><li>Transform design briefs and user stories into interactive prototypes and conversational interfaces that balance empathy, intent, and usability. </li></ul><div> </div><div><strong>Course overview</strong>  </div><div>During this course, you’ll explore leading tools such as UXPilot, Miro, Visily, Uizard, and Figma. You’ll learn how to use GenAI to streamline tasks such as generating personas and journey maps, crafting UX microcopy, and simulating chatbot interactions. And you’ll discover how to ensure that your designs are human-centered, inclusive, and accessible. </div><div> </div><div>Through guided labs, you’ll gain practical experience in AI-assisted wireframing, usability testing, and conversational UX design. And for your final project, you’ll design an AI-powered experience, apply prompt engineering techniques, and refine your solution through iterative feedback loops. </div><div> </div><div>If you’re keen to leverage GenAI as your collaborative design partner to work faster, smarter, and more creatively, enroll today! </div><div> </div><div> </div><div><strong>Prerequisites</strong> </div><div>This course is designed for aspiring and practicing UI/UX designers, product designers, researchers, and creative professionals interested in applying generative AI to their design work. A basic understanding of UI, UX, and generative AI concepts is recommended. Familiarity with design tools, prompts, and user research methods will help you maximize the benefits of the hands-on projects. </div><div> </div><div><strong>Related</strong><em> </em><strong>Professional Certificate</strong> </div><div>This course is part of the IBM UI/UX Designer Professional Certificate. If you’re looking to master UI/UX design and use generative AI to transform your design workflows, we recommend enrolling in the full program and completing the courses in sequence. In just a few months, you’ll build job-ready UX/UI skills, create a professional design portfolio, and stand out to employers in the growing field of digital design.   </div><div> </div><div><strong>Course syllabus</strong> </div><div>The course consists of the following modules </div><ul><li>Foundations of UI UX Design with Generative AI </li><li>AI-Powered UX Research and Analysis </li><li>AI-Driven Prototyping and Conversational UX </li><li>Final Project and Final Quiz </li></ul>
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Generative AI Skills for UI/UX Design

Transform your UX/UI workflow with generative AI. Use tools such as Figma, Uizard & Miro to design AI-powered prototypes, chatbots & user experiences.

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  <div>Ready to take your UI/UX design skills to the next level with Figma?  Figma has become an industry standard across companies of all sizes, and mastering it is essential for any UI/UX design career. This course gives you job-ready skills in wireframing, prototyping, and usability testing using Figma; essential skills for your resume! </div><div> </div><div><strong><em>You will:</em></strong>  </div><ul><li>Design a portfolio-ready, end-to-end UX project in Figma that showcases your ability to plan, structure, and deliver user-centered digital experiences. </li><li>Create low- to high-fidelity wireframes and interactive prototypes using grids, components, and advanced layout techniques aligned with industry workflows. </li><li>Conduct usability testing and apply Gestalt principles, responsive design, and accessibility standards to refine and optimize cross-device user experiences. </li><li>Learn to collaborate effectively in Figma using shared libraries, version control, and design systems to maintain consistency and efficiency in team-based projects. </li></ul><div> </div><div><strong>Course overview</strong> </div><div> </div><div> </div><div>During this course, you’ll get hands-on designing user flows, structured wireframes, and interactive prototypes, including those with gesture-based interfaces. </div><div> </div><div>You’ll apply Gestalt principles to create strong visual hierarchy, organize content through effective information architecture, and adopt a mobile-first design approach. Plus, through real-world challenges and usability testing, you’ll refine your designs based on feedback and learn to solve UX problems with confidence and clarity. </div><div> </div><div>In a final project, you’ll then create a high-fidelity prototype and your own reusable design system; great experience to chat about in interviews!  </div><div>Enroll today and build Figma skills in wireframing and prototyping that can power your UI/UX career forward.  </div><div><strong>Prerequisites</strong> </div><div>This course is designed for beginners or experienced professionals looking to transition into or deepen their expertise in UI/UX design. Learners are expected to have a basic understanding of UI/UX principles and design concepts. While no prior hands-on design experience is required, familiarity with fundamental design principles is recommended to make the most of the hands-on activities in this course. </div><div> </div><div><strong>Related</strong><em> </em><strong>Professional Certificate</strong> </div><div> </div><div>This course is part of the <strong>IBM UI/UX Designer Professional Certificate</strong>. If you’re looking to master UI/UX design, including using Figma, prototyping, and usability testing, we recommend enrolling in the full program and progressing through the courses in sequence. In just a few months, you’ll build job-ready UX/UI skills, create a professional design portfolio, and stand out to employers in the growing field of digital design. </div><div> </div><div><strong>Course syllabus</strong> </div><div>The course consists of the following modules: </div><ul><li>Wireframing Fundamentals with Figma </li><li>Prototyping and Usability Testing </li><li>UI Patterns and Design Systems </li><li>Interactive and Gesture-Based UI </li><li>Final Project and Final Exam </li></ul><div> </div>
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UI/UX Design: Wireframing & Prototyping with Figma

Master Figma essentials for designing user-centered prototypes. Create structured wireframes, gesture-based interfaces, and Hi-Fi designs for your UI/UX portfolio.

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  <div>The U.S. Bureau of Labor Statistics projects about 12-13% job growth for computer systems and network architects in the coming years. This course is your first step toward entering the field of IT systems architecture. <br /><br /></div><div>You’ll begin looking at systems and solutions architects’ roles, responsibilities, and skills, and exploring career paths with certifications to enhance your expertise. <br /><br />You'll then explore systems thinking, identify key system components, and learn how they interact within an organization. You'll also analyze business processes and apply the systems development life cycle (SDLC) to guide project planning. <br /><br />Next, you’ll analyze systems/software architecture and design principles to visualize system structure with tools like UML, apply object-oriented analysis, and grasp architectural patterns such as layered, client-server, and microservices. Plus, you’ll examine deployment models and production environments to learn how real-world systems are delivered.  <br /><br />You’ll work with tools like data flow diagrams (DFDs), entity-relationship diagrams (ERDs), and prototyping software to support documentation and stakeholder communication.  <br /><br />Through hands-on labs and a project, you'll start to design scalable, maintainable systems tailored to business needs.  <br /><br />Enroll today to kickstart your journey to becoming a successful systems or solutions architect!<br /><br /></div><h1><strong>What will you learn?</strong></h1><ul><li>Explore the exciting world of systems architecture and learn the fundamentals to design scalable, reliable system solutions </li><li>Explain the solutions architect’s role, required skills, career paths, certifications, and how they use systems thinking and SDLC to meet business needs </li><li>Gain hands-on experience by applying systems design fundamentals and tools to design IT systems and solutions</li><li>Utilize requirement-gathering, prototyping, and modeling tools to analyze business needs and develop documented solutions for real-world applications </li></ul><h1>Course Syllabus</h1><div>This course comprises the following four modules: <br /><br /></div><div><strong>Module 1: Introducing the Systems &amp; Solutions Architect Roles </strong></div><ul><li>Lesson 0: Welcome </li><li>Lesson 1: Getting Started with the Systems Architect Role </li><li>Lesson 2: Module Glossary and Assessment </li></ul><div><strong>Module 2: Introduction to Systems Analysis and Architecture </strong></div><ul><li>Lesson 1: What is a System? </li><li>Lesson 2: Understanding Systems and the Analyst's Role in Business </li><li>Lesson 3: Module Glossary and Assessment  </li></ul><div><strong>Module 3: Software Architecture, Design, and Patterns </strong></div><ul><li>Lesson 1: Software Architecture and Design </li><li>Lesson 2: Software Architecture Patterns and Deployment Technologies </li><li>Lesson 3: Module Glossary and Assessment </li></ul><div><strong>Module 4: Module 4: Tools Used by Systems Architects </strong></div><ul><li>Lesson 1: Tools Used by Systems Architects </li><li>Lesson 2: Applying Systems Architect Tools in Practice </li><li>Lesson 3: Module Glossary and Assessment </li></ul><div><strong>Module 5: Final Project, Final Quiz, and Course Wrap-Up </strong></div><ul><li>Lesson 1: Final Project </li><li>Lesson 2: Course Glossary, Final Quiz, and Wrap-Up </li></ul>
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Introduction to Systems Architecture

This course offers a foundational introduction to systems architecture and the essential principles behind designing robust, scalable IT systems. Learners will explore the role of the systems architect, gaining insight into how they bridge business needs with technical solutions. Through a combination of videos, expert insights, and hands-on activities, the course covers core topics such as system components, system development life cycle (SDLC) models, software design, and architectural patterns.

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  <div>Take on the role of a UI/UX designer and bring a real-world project to life in this hands-on capstone course. <br /><br /></div><div>You’ll apply the full design thinking process, from conducting user research to building high-fidelity, accessible interfaces. Create wireframes and interactive prototypes in Figma, design scalable systems, and prepare developer-ready handoffs. <br /><br /></div><div>By course completion, you’ll have a polished project demonstrating your ability to craft user flows, test and iterate designs, and apply industry-standard practices. You’ll also learn to present your work through a professional case study, build a compelling UX resume, and develop a portfolio ready for potential employers and clients. <br /><br /></div><div>This capstone focuses on designing an e-commerce platform from concept to final design, giving you practical experience and tangible work to showcase your skills. <br /><br /></div><div>Step into a design role, pitch your ideas confidently, and advance your design career with a portfolio that stands out—enroll now! </div><div><br /></div><h1>Learning Objectives</h1><div>• Take a real-world UX/UI project from research to polished high-fidelity designs using the full design thinking process. </div><div>• Plan, prototype, and test intuitive, user-focused interfaces with Figma and other industry-standard UX tools. </div><div>• Build scalable design systems and developer-ready handoffs that prioritize accessibility. </div><div>• Showcase your skills through a professional case study, portfolio, and UX-focused resume to impress potential employers. <br /><br /></div><h1>Course Syllabus</h1><div>Welcome to the Course </div><ul><li>Video: Course Introduction </li><li>Reading: Course Overview </li><li>Reading: Helpful Tips for Course Completion </li></ul><div> </div><div>Module 1: Getting Started with the Capstone Project </div><div>Module Summary and Learning Objectives </div><div>Lesson 1: Project Overview and Expectations </div><ul><li>Video: Project Overview </li><li>Reading: Capstone Project: Getting Started Guide </li><li>Reading: Capstone Tools and Workflow Checklist </li><li>Reading: Capstone Project Brief </li><li>Reading: Project Scoping Guide </li><li>Lab: Analyzing the Project Brief </li><li>Lab: Creating a Project Scope Summary </li></ul><div>Lesson 2: Module Summary and Assessments </div><ul><li>Reading: Summary and Highlights: Getting Started with the Capstone Project </li><li>Practice Quiz: Checklist: Project Overview and Expectations </li><li>Graded Quiz: Project Overview and Expectations </li></ul><div> </div><div>Module 2: Empathize and Define </div><div>Module Summary and Learning Objectives </div><div>Lesson 1: Conducting UX Research </div><ul><li>Reading: Competitive Analysis Tips and Templates </li><li>Lab: Performing Competitive Analysis </li><li>Reading: User Interview Planning Guide </li></ul><div>Lesson 2: Synthesizing Research </div><ul><li>Reading: Empathy Map and Persona Templates </li><li>Lab: Creating an Empathy Map </li><li>Lab: Developing a User Persona </li><li>Reading: Framing Problems and Identifying Opportunities </li><li>Lab: Writing a Problem Statement </li></ul><div>Lesson 3: Module Summary and Assessments </div><ul><li>Summary and Highlights: Empathize and Define </li><li>Practice Quiz: Checklist: Empathize and Define </li><li>Graded Quiz: Empathize and Define </li></ul><div> </div><div>Module 3: Ideate and Plan </div><div>Module Summary and Learning Objectives </div><div>Lesson 1: Structuring Ideas </div><ul><li>Reading: Best Practices for Sketches, Flowcharts, and Information Architecture </li><li>Lab: Creating Initial Sketches </li><li>Lab: Creating User and Task Flows </li><li>Lab: Create an Initial Sitemap </li></ul><div>Lesson 2: Planning Your Design  </div><ul><li>Reading: Key Feature Planning and Prioritization Matrix Guide </li><li>Lab: Identifying Key Versus Optional Features </li><li>Lab: Developing a Prioritization Matrix </li><li>Reading: Examples of Success Metrics </li><li>Lab: Defining Success Metrics   </li><li>Lab: Creating a Wireframe Planning Outline </li></ul><div>Lesson 3: Module Summary and Assessments </div><ul><li>Summary and Highlights: Ideate and Plan </li><li>Practice Quiz: Checklist: Ideate and Plan </li><li>Graded Quiz: Ideate and Plan </li></ul><div> </div><div>Module 4: Wireframing and Prototyping </div><div>Module Summary and Learning Objectives </div><div>Lesson 1: Building Wireframes </div><ul><li>Reading: Wireframe Guidelines and Layout Tips </li><li>Lab: Designing Wireframe Screens in Figma </li></ul><div>Lesson 2: Creating Interactive Prototypes </div><ul><li>Reading: Prototyping Guide: Links, Components, and States </li><li>Lab: Connecting Wireframes Screens </li><li>Lab: Adding Interactions Using Components and Variants </li></ul><div>Lesson 3: Module Summary and Assessments </div><ul><li>Summary and Highlights: Wireframing and Prototyping </li><li>Practice Quiz: Checklist: Wireframing and Prototyping </li><li>Graded Quiz: Wireframing and Prototyping </li></ul><div> </div><div>Module 5: Usability Testing and Iteration </div><div>Module Summary and Learning Objectives </div><div>Lesson 1: Testing Your Prototype </div><ul><li>Reading: Usability Test Planning Guide and Template </li><li>Lab: Conducting Usability Tests </li><li>Reading: Task Writing Template and Feedback Tips </li><li>Lab: Translating and Summarizing Feedback </li></ul><div>Lesson 2: Making Iterations </div><ul><li>Reading: Severity Rating Scale and Design Fix Guidelines </li><li>Lab: Prioritizing Feedback </li><li>Lab: Iterating and Updating a Prototype </li></ul><div>Lesson 3: Module Summary and Assessments </div><ul><li>Summary and Highlights: Usability Testing and Iteration </li><li>Practice Quiz: Checklist: Usability Testing and Iteration </li><li>Graded Quiz: Usability Testing and Iteration </li></ul><div> </div><div>Module 6: Final User Interface Design and Handoff  </div><div>Module Summary and Learning Objectives </div><div>Lesson 1: Creating a Design System and Hi-Fi User Interface </div><ul><li>Reading: Style Guide and UI Components Guides and Tips </li><li>Lab: Building a Style Guide and Component Library </li><li>Reading: High-Fidelity Mock-Ups Tips and Guides </li><li>Lab: Designing High-Fidelity Mock-Ups </li></ul><div>Lesson 2: Developer Handoff  </div><ul><li>Reading: Handoff Preparation Guide </li><li>Lab: Preparing for Developer Handoff </li></ul><div>Lesson 3: Module Summary and Assessments </div><ul><li>Summary and Highlights: Final UI Design and Handoff </li><li>Practice Quiz: Checklist: Final User Interface Design and Handoff </li><li>Graded Quiz:  Final User Interface Design and Handoff </li></ul><div>Lesson 4: Project Submission and Evaluation </div><ul><li>Reading: Preparing Your Project Submission </li><li>Final Project: Part I - Submission and Evaluation </li><li>Final Project: Part II - Submission and Evaluation </li></ul><div> </div><div>Module 7: [Bonus] Launch Your UI/UX Career and Course Wrap-up </div><div>Module Summary and Learning Objectives </div><div>Lesson 1: Building Your Resume and Portfolio </div><ul><li>Video: Resume Writing Guide </li><li>Lab: Drafting a UI/UX Resume </li><li>Video: Writing and Building a Portfolio: Case Study Tips </li><li>Lab: Drafting a Case Study Summary Based on the Capstone Project </li><li>Lab: Building a Case Study </li></ul><div>Lesson 2: Exploring Career Paths and Interview Preparation </div><ul><li>Video: Freelancing and Job Search Guide </li><li>Lab: Writing a Freelance and Job-Ready Profile </li><li>Video: Common UI/UX Interview Questions and Tips </li></ul><div>Lesson 3: Module Summary </div><ul><li>Summary and Highlights: Launch Your UI/UX Career </li><li>Practice Quiz: Checklist: Launch Your UI/UX Career </li></ul><div>Lesson 4: Course Wrap-Up </div><ul><li>Reading: Congratulations and Next Steps </li><li>Reading: Team and Acknowledgments </li><li>Feedback Form </li></ul><div><br /></div><h1>General Information</h1><div>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari. </div><div><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>The following skills are required to be successful with this course:  </div><ul><li>We recommend completing all previous courses in the IBM UI/UX Designer Professional Certificate before starting this capstone. </li></ul>
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UI/UX Design: Capstone Project

Step into a UI/UX designer role and complete a hands-on capstone project. Apply the full design thinking process, create prototypes in Figma, and build a portfolio-ready showcase for an e-commerce platform. 

<div>Prepare for a career in the high-demand field of digital marketing with the IBM Digital Marketing and Growth Hacking Professional Certificate. This program equips you with the most up-to-date skills and tools needed to succeed as a digital marketer or growth hacker, with no prior experience required.<br><br>In this Professional Certificate, you will:&nbsp;</div><ul><li>Master practical, up-to-date skills in digital marketing, SEO, content marketing, paid advertising, and social media.</li><li>Apply growth hacking techniques to design, test, and optimize multi-channel campaigns for maximum reach and ROI.</li><li>Create, manage, and analyze data-driven marketing campaigns across major platforms like Google, LinkedIn, and YouTube.</li><li>Leverage GenAI to automate and personalize marketing content, including ad copy, product recommendations, and email campaigns.</li><li>Develop and optimize e-commerce strategies and conversion-focused product pages.</li><li>Build a professional portfolio featuring projects that showcase job-ready digital marketing and analytics skills.</li><li>Complete a real-world capstone project integrating AI-enhanced strategies, analytics, and presentation skills.</li></ul><div><br>This program is ideal for anyone interested in starting a career in digital marketing as well as entrepreneurs and small business owners who want to learn how to effectively leverage online marketing channels for growth.<br><br></div><div>You’ll master key areas such as digital advertising, search engine optimization (SEO), content marketing, social media management, email marketing, and data analytics. Through interactive projects and labs, you’ll gain the skills employers look for in entry-level roles and build a portfolio that demonstrates your hands-on expertise.<br><br></div><div>You’ll learn how to design and optimize campaigns across multiple channels, leveraging modern marketing tools such as Google Ads, Meta Advertising, and Yoast SEO. You’ll also gain practical experience with e-commerce optimization, influencer marketing, and schema markup creation. To stay ahead of industry trends, you’ll use generative AI tools like ChatGPT and Gemini to automate and enhance tasks in SEO, content creation, and campaign management.<br><br></div><div>The program culminates in a capstone project where you’ll design and execute a comprehensive digital marketing strategy for a hypothetical brand, integrating all the skills you’ve developed. You’ll leave the program with a portfolio-ready campaign, an IBM Professional Certificate, and a verified IBM Digital Badge showcasing your job-ready expertise.<br><br></div><div>Career resources, such as mock interviews, resume optimization, and LinkedIn support, are included to help you confidently pursue roles like Digital Marketing Specialist, SEO Strategist, Content Marketer, or Growth Hacker. Whether you’re beginning your career or pivoting into digital marketing, this certificate provides the knowledge and credibility to stand out in a competitive field.<br><br><strong>Applied Learning Project<br><br></strong>Throughout the program, you’ll complete projects and labs to gain a firm grasp on the required skills commonly used by digital marketers.<br><br>Examples of labs and projects included in the program:</div><ul><li>Develop advertising campaigns for various social media channels, including Google, Instagram, Facebook, and LinkedIn</li><li>Optimize a campaign based on data</li><li>Perform influencer marketing selection</li><li>Create and optimize rich snippets and schema markups</li><li>Build campaigns on Google AdWords</li><li>Optimize e-commerce campaigns using Google Shopping</li><li>Generate personalized product recommendations</li><li>Leverage GenAI tools in digital marketing areas, such as content creation, email marketing, SEO, and social media</li></ul><div><br>The program includes a capstone project that provides a hands-on opportunity to apply your skills in a real-world scenario, guiding you to develop a comprehensive digital and growth hacking marketing strategy.<br><br>No college degree or prior experience is required. All you need is a willingness to learn, and have the basic computer literacy to take online courses.&nbsp;</div>

IBM Digital and Growth Marketing Professional Certificate

Launch your career in digital marketing with IBM. Gain job-ready skills in SEO, social media, content creation, paid ads, and data analytics. Learn AI-driven growth hacking and earn your IBM Professional Certificate in under 3 months.

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  <div>Ready to build the strong creative foundation you need for a successful UI/UX career? This beginner-friendly course helps you master essential design principles, user-centered methods, and hands-on prototyping skills using tools like Miro and Figma. It’s ideal for aspiring designers and career switchers who are keen to build job-ready UX/UI design skills and demonstrate them in a portfolio-worthy project. <br /> </div><div><strong><em>You will:</em></strong>  </div><ul><li>Conduct user research, competitor analysis, and heuristic evaluations to identify usability gaps and deliver better user experiences. </li><li>Apply typography, color theory, and layout principles to create visually cohesive, accessible, and intuitive designs. </li><li>Build interactive prototypes and microinteractions in Figma, Miro, and Draw.io to demonstrate advanced design and prototyping skills. </li><li>Design a portfolio-ready, user-centered interface project that showcases your ability to solve real-world UX/UI challenges and impress potential employers. </li></ul><div> </div><div><strong>Course overview</strong> </div><div>During this course, you’ll master essential UI/UX tasks, including conducting heuristic evaluations to identify usability issues, using user journey mapping to optimize flows, and applying A/B testing for data-driven design improvements. You’ll also explore microinteraction design to enhance engagement and learn to create responsive interfaces that ensure consistency across devices. </div><div> </div><div>Throughout the course, you’ll work hands-on with industry-standard tools, such as Figma and Miro, tackling real-world design challenges and usability testing exercises. For your final project, you’ll design an interactive prototype for a health and fitness app. You’ll then conduct tests, gather feedback, and refine your design to produce a polished, portfolio-ready product. </div><div> </div><div>By the end, you’ll have the UX/UI design foundation and practical experience employers look for. Enroll now to start creating impactful, user-centered digital experiences. </div><div> </div><div><strong>Prerequisites</strong> </div><div>This course is designed for both UX/UI beginners and experienced professionals looking to deepen their design expertise . A basic familiarity with digital tools and an understanding of visual or interaction design principles can be helpful, but are not essential. No prior experience in UX/UI design is necessary to get started. </div><div> </div><div><strong>Related</strong><em> </em><strong>Professional Certificate</strong> </div><div>This course is part of the <strong>IBM UI/UX Designer Professional Certificate</strong>. If you’re looking to build hands-on skills in user research, interaction design, and usability testing, we recommend enrolling in the full program and progressing through the courses in order. In just a few months, you’ll gain job-ready UX/UI design experience, build a professional portfolio, and stand out to employers in the fast-growing design industry. </div><div> </div><div><strong>Course syllabus</strong> </div><div>The course consists of the following modules </div><ul><li>Introduction to UX/UI and Design Thinking </li><li>Usability and Interaction Design </li><li>Visual Design and Aesthetic Usability </li><li>Final Project and Final Exam </li></ul>
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Build hands-on UI/UX skills in design thinking, user flows, A/B testing, and prototyping. Design interactive apps with Figma & Miro. Conduct usability testing. 

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  <div>This hands-on project introduces you to <strong>building AI-interactive web apps</strong> using <strong>Gradio and GPT-5</strong>. You’ll create a <strong>Halloween-themed quiz game</strong> that automatically generates fresh, themed questions using GPT-5 — from spooky riddles to horror movie trivia.<br />Players can use lifelines like <strong>50-50</strong>, <strong>Flip Question</strong>, or <strong>Pass</strong>, and the app will track their score and show final results at the end.</div><div><br />You’ll learn how to design interactive interfaces using <strong>Gradio Components</strong>, connect GPT-5 for <strong>real-time content generation</strong>, and handle user logic such as scoring, timers, and state persistence. The result is a fun, AI-driven game that demonstrates real-world concepts of LLM-based interactivity, event handling, and creative user experience design.<br /><br /></div><h1><strong>Who Should Enroll</strong></h1><ul><li>Beginners in AI or Python looking for a creative, hands-on project</li><li>Developers who want to learn Gradio for building quick AI demos</li><li>Educators or students who want to design interactive AI-based games</li><li>Anyone curious about combining GPT models with real-time user interfaces</li></ul><h1><strong>Why Enroll</strong></h1><div>In just 30 minutes, you’ll go from idea to execution — building a <strong>complete AI quiz game</strong> that can be hosted locally or shared via Gradio’s public URL. You’ll understand how to turn GPT-5 prompts into dynamic gameplay elements and walk away with a working interactive app that’s fun, educational, and portfolio-ready.</div><div>Whether you’re showcasing your AI skills or just want to create something unique for Halloween, this project gives you a perfect blend of <strong>learning and entertainment</strong>.</div><div><br /><br /></div><h1><strong>A Look at the Project Ahead</strong></h1><div>After completing this guided project, you will be able to:</div><ul><li>Understand Gradio’s architecture — Build and organize components for user input, output, and UI flow.</li><li>Generate AI-based quiz questions — Use GPT-5 to create creative, random Halloween-themed questions.</li><li>Implement game logic — Manage lifelines (50-50, flip, pass), scoring, and player progression.</li><li> Host and share — Run your quiz locally or deploy it with Gradio’s public share link for friends or students to play.</li></ul><h1><strong>What You’ll Need</strong></h1><div><strong>Technical Requirements:</strong></div><ul><li>Basic knowledge of Python programming</li><li>No prior AI or web development experience required</li></ul><div><strong>Browser Setup:</strong></div><ul><li>A modern web browser (e.g., Chrome, Firefox) to access Gradio interface.</li><li> Internet connection for GPT-5 model access.</li></ul><div><br /></div>
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Build an AI-Powered Halloween Quiz Game with Gradio & GPT-5

Create a spooky AI-powered Halloween Quiz Game using the powerful UI framework Gradio and OpenAI’s GPT-5 model. Learn how to auto-generate themed questions, add lifelines like 50-50 and Flip Question, track player scores, and design an interactive game interface. Build and launch a fully functional AI-driven web app without writing complex frontend code — the perfect beginner-friendly project that blends Python, AI, and creativity into one engaging experience.

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  <div> Want to get hands-on using Wireshark – the world’s leading network protocol analyzer? This course is ideal for students, cybersecurity beginners, IT professionals, and network administrators who want to strengthen their ability to <strong>monitor, diagnose, and defend digital networks</strong>. You’ll build the in-demand skills you need to analyze, secure, and troubleshoot network traffic using the popular Wireshark application. <br /> </div><div>After completing this course, you will be able to: </div><ul><li>Capture and analyze real-time network traffic using Wireshark to detect anomalies and threats. </li><li>Apply packet filtering, deep packet inspection (DPI), and protocol analysis for cybersecurity investigations. </li><li>Recognize and report vulnerabilities and suspicious activity across network environments. </li><li>Generate actionable network reports to support incident response and forensics. </li></ul><div> <strong>Course Overview</strong> </div><div>During the course, you’ll develop the analytical mindset and technical proficiency needed to investigate network behavior and identify potential security threats. </div><div>You’ll start by exploring <strong>network forensics and traffic analysis</strong>, learning how they support cybersecurity and ethical hacking operations. Through videos, readings, and case studies, you’ll examine real-world applications and ethical considerations in network monitoring. Then, you’ll move on to <strong>packet capture and filtering using Wireshark</strong>, where you’ll install, configure, and navigate the Wireshark interface, apply traffic filters, and interpret packet headers to uncover valuable insights. </div><div>You’ll then dive into <strong>Deep Packet Inspection (DPI)</strong> and <strong>protocol analysis</strong>, using Wireshark, learning to dissect network protocols, identify vulnerabilities, and map findings to security frameworks.  </div><div>As you progress, hands-on <strong>labs, activities, and case studies</strong> will reinforce each concept. You’ll practice scanning, filtering, inspecting packets, and creating professional reports using Wireshark’s advanced features. Then, in the <strong>final project, </strong> you’ll<strong> </strong>apply your knowledge in <strong>advanced traffic analysis</strong>, where you’ll analyze flow data, recognize bandwidth issues, identify potential breaches, and generate detailed reports for stakeholders. Great for demonstrating your ability to apply forensic and analytical skills to real-world scenarios! </div><div> </div><div>If you’re keen to build the confidence and expertise to perform <strong>network traffic analysis, forensic investigations, and threat detection</strong> using Wireshark, enroll today! </div><div><strong>Prerequisites</strong> </div><div>This is an <strong>intermediate-level</strong> course. Basic understanding of computer networks, IP addressing, and cybersecurity concepts is recommended. Prior experience with Linux or network tools is helpful but not required. </div><div> </div><div><strong>Related Professional Certificates</strong> </div><div>This course is part of the IBM Cybersecurity Analyst Professional Certificate and complements courses in the Ethical Hacking and Network Security learning paths. Completing these programs can help you prepare for roles such as Network Traffic Analyst, Cybersecurity Technician, Security Operations (SOC) Analyst, or Forensics Investigator. </div><div> </div><div><strong>Course Syllabus</strong> </div><ul><li><strong>Welcome to the Course</strong> </li><li>Course Introduction, Overview, and Learning Objectives </li><li><strong>Module 1: Network Forensics and Network Traffic Analysis</strong> </li><li>Fundamentals, Evolution, and Case Studies of Network Forensics </li><li>Ethical and Legal Considerations </li><li>Network Traffic Analysis vs. Network Forensics </li><li>Practice and Graded Quizzes </li><li><strong>Module 2: Packet Capture and Filtering with Wireshark</strong> </li><li>Installing, Configuring, and Navigating Wireshark </li><li>Capturing, Scanning, and Filtering Network Traffic </li><li>Advanced Filtering Techniques and Troubleshooting </li><li>Labs on Packet Capture and Filter Configuration </li><li><strong>Module 3: DPI and Network Protocol Analysis</strong> </li><li>Performing Deep Packet Inspection (DPI) </li><li>Mapping DPI to Security Frameworks </li><li>Detecting Vulnerabilities in Common Network Protocols </li><li>Labs on Manual DPI and Protocol Analysis </li><li><strong>Module 4: Advanced Traffic Analysis and Reporting</strong> </li><li>Flow Data, NetFlow, and Bandwidth Analysis </li><li>Identifying Threats and Breaches in Network Traffic </li><li>Exporting Captured Data and Reporting Best Practices </li><li>Labs on Data Export and Report Generation </li><li><strong>Module 5: Project and Final Exam</strong> </li><li>Final Project: Capture, Analyze, and Respond to Network Traffic </li><li>Final Quiz and Submission </li><li><strong>Module 6: Course Wrap-Up and Feedback</strong> </li></ul><div> </div>
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Wireshark for Network Traffic Analysis

Master Wireshark and build in-demand skills to capture, analyze, and secure network traffic. Plus, learn packet analysis, DPI, and reporting to detect and respond to threats.

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  <div> Want to learn how to create a strategic e-commerce plan, enhance product listings with compelling content and visuals, and gain insights to make data-driven decisions? This beginner-friendly course gives aspiring marketers, freelancers, entrepreneurs, and small business owners the valuable skills needed to drive business success through e-commerce and email campaigns. <br /><br /></div><div>After completing this course, you will be able to: </div><ul><li>Drive engagement, conversions, and business growth through successful e-commerce marketing and email campaigns </li><li>Develop an e-commerce strategy and implement proven techniques that achieve online results </li><li>Enhance product listings to boost visibility and conversion rates using e-commerce and email analytics tools, including generative AI </li><li>Craft engaging, personalized, and visually appealing email campaigns </li></ul><div><br /></div><div><strong>Course Overview</strong> </div><div>  </div><div>During this course, you’ll discover how to define a value proposition, craft an e-commerce strategy, and select the right platform for a business. You’ll learn how to optimize product listings with persuasive content, visuals, and targeted keywords. Plus, you’ll master email marketing essentials, including audience segmentation, GDPR, CAN-SPAM, and personalization techniques.  </div><div> </div><div>Through hands-on practice using tools like Google Analytics and generative AI, you’ll learn how to track KPIs, analyze customer behavior, and calculate metrics to make data-driven decisions. Plus, you’ll experiment with email marketing and A/B testing tools to refine campaigns and apply growth hacking techniques to boost ROI.  </div><div> </div><div>If you're keen to grow an e-commerce business and harness the power of email marketing, enroll today! </div><div> </div><div><strong>Prerequisites</strong> </div><div>This is a beginner-friendly course. No prior experience is required.  </div><div> </div><div><strong>Related Professional Certificates</strong> </div><div> </div><div>This course is part of the <strong>Digital Marketing and Growth Hacking Professional Certificate </strong>and <strong>Online Marketing and Growth Hacking Specialization. </strong>These<strong> </strong>programs are<strong> </strong>designed to to give you the job-ready skills you need to run successful digital marketing campaigns and growth hacking initiatives. <br /> </div><div><strong>Course Syllabus</strong> </div><div><strong>Module 0: Welcome </strong> </div><ul><li>Video: Course Introduction </li><li>Reading: Learning Objectives and Syllabus </li><li>Reading: General Information </li><li>Reading: Grading Scheme </li><li>Reading: How to Make the Most of This Course </li></ul><div><strong>Module 1: E-Commerce Fundamentals and Strategy</strong> </div><ul><li>Reading: Module 1 Introduction and Learning Objectives </li><li>Video: E-Commerce: The Digital Marketplace </li><li>Reading: E-Commerce: A Beginner's Guide  </li><li>Video: Key Components of an E-Commerce Business  </li><li>Reading: Exploring E-Commerce Platforms  </li><li>Lab: Creating Your Online Store  </li><li>Video: Defining Your Niche and Target Audience  </li><li>Reading: Understanding the E-Commerce Customer Journey  </li><li>Video: Value Proposition and Competitive Advantage </li><li>Reading: Setting Goals and Key Performance Indicators (KPIs)  </li><li>Lab: Analyzing E-Commerce Business Examples  </li><li>Reading: Summary and Highlights: E-Commerce Fundamentals and Strategy  </li><li>Assessment: Practice Quiz: E-Commerce Fundamentals and Strategy  </li><li>Assessment: Graded Quiz: E-Commerce Fundamentals and Strategy  </li></ul><div><strong>Module 2: E-Commerce Practices</strong> </div><ul><li>Reading: Module 2 Introduction and Learning Objectives </li><li>Video: Crafting Compelling Product Titles </li><li>Video: Writing Persuasive Product Descriptions  </li><li>Reading: The Power of High-Quality Product Images  </li><li>Video: Customer Reviews and Trust Building  </li><li>Reading: Leveraging Product Variations and Attributes  </li><li>Lab: Store Optimizations- Analyze and Improve E-Commerce Performance </li><li>Video: Conversion Rates and Sales Funnel  </li><li>Video: Key Elements of a High-Converting Product Page  </li><li>Reading: Optimizing the Checkout Process for Higher Conversions  </li><li>Video: Product Recommendations </li><li>Reading: Improving Website Navigation and Site Search  </li><li>Lab: Conversion Rate Optimization through A/B Testing </li><li>Video: Introduction to E-Commerce Analytics  </li><li>Reading: Understanding Customer Behavior  </li><li>Video: Understanding Google Analytics for E-Commerce  </li><li>Reading: Analyzing Product Performance and Sales Data  </li><li>Lab: Exploring E-Commerce Analytics Dashboards </li><li>Reading: Summary and Highlights: E-Commerce Practices </li><li>Assessment: Practice Quiz: E-Commerce Practices </li><li>Assessment: Graded Quiz: E-Commerce Practices </li></ul><div><strong>Module 3: Email Marketing Professionals</strong> </div><ul><li>Reading: Module 3 Introduction and Learning Objectives </li><li>Video: Introduction to Email Marketing  </li><li>Video: Key Terminology and Concepts  </li><li>Reading: Building Your Email List  </li><li>Lab: Setting Up an Email Marketing Campaign  </li><li>Reading: Compliance and Regulations  </li><li>Video: Writing Compelling Subject Lines  </li><li>Reading: Creating High-Converting Email Content  </li><li>Video: Designing Visually Appealing Emails </li><li>Reading: Personalization and Segmentation in Emails  </li><li>Lab: Testing Email Content for Performance </li><li>Video: Email Marketing Platforms  </li><li>Reading: Setting Up and Integrating an Email Marketing Platform  </li><li>Video: Key Email Marketing Metrics and KPIs  </li><li>Reading: Analyzing Email Campaign Performance  </li><li>Lab: Interpreting Email Campaign </li><li>Reading: Summary and Highlights: Email Marketing Fundamentals  </li><li>Assessment: Practice Quiz: Email Marketing Fundamentals </li><li>Assessment: Graded Quiz: Email Marketing Fundamentals </li></ul><div><strong>Module 4: Final Project and Exam</strong> </div><ul><li>Reading: Module 4 Introduction and Learning Objectives </li><li>Final Project: Launching an E-commerce Brand with Email Marketing’  </li><li>Project: Final Submission </li><li>Reading: Course Glossary </li><li>Assessment: Final Quiz: E-commerce Marketing and Email Campaigns </li><li>Video: Course Wrap-Up </li><li>Reading: Congratulations and Next Steps </li><li>Reading: Team and Acknowledgments </li></ul><div><br /><br /></div><div><strong><br /></strong><br /></div>
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Digital Marketing: E-commerce & Email Campaigns

Master e-commerce and email marketing! Learn to build online stores, optimize campaigns, boost conversions, and use analytics for data-driven growth. 

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  <div>Dive into the world of digital advertising and learn to design successful campaigns through planning and optimizing Google search campaigns, search engine marketing, and ad grouping. This course gives aspiring marketers, freelancers, small business owners, and D2C founders the critical digital marketing skills required to design and manage digital marketing campaigns that drive business success. </div><div> </div><div>After completing this course, you will be able to: </div><ul><li>Run successful digital advertising campaigns using fundamental digital advertising concepts and strategies </li><li>Apply the principles of Search Engine Marketing (SEM) by conducting keyword research, setting up Google Search campaigns, and optimizing for better ROI  </li><li>Create and manage effective social media advertising campaigns using platform-specific tools and best practices </li><li>Analyze influencer marketing strategies and evaluating campaign effectiveness through relevant metrics </li></ul><div><strong>Course Overview</strong> </div><div> </div><div>During this course, you’ll explore the core elements of digital advertising, including search engine marketing (SEM), keyword search, bidding strategies, and ad grouping. You’ll learn how to plan and optimize Google search campaigns, and explore B2B marketing, lead generation, advertising strategies, audience targeting, and ad formats across social media platforms. </div><div> </div><div>You’ll also gain insights into Google Shopping Campaigns and influencer marketing, including how to select, evaluate, and collaborate with influencers strategically. Plus, you’ll explore how generative AI can transform your work, adding creativity and efficiency.  </div><div> </div><div>Additionally, you’ll apply your skills through hands-on labs and real-world projects, developing data-driven strategies to create and optimize campaigns using tools like Shown.io and Facebook Ad Account to maximize return on investment (ROI).   </div><div> </div><div>If you’re keen to build high-performing ad campaigns that generate real results, ENROLL today!  <br /><br /></div><div><strong>Prerequisites</strong> </div><div>This is a beginner-friendly course. No prior experience is required. However, you should have basic computer skills and be familiar with MS Office. It is advantageous to have a fundamental knowledge of marketing concepts.   </div><div> </div><div><strong>Related Professional Certificates</strong> </div><div> </div><div> This course is part of the <strong>Digital Marketing and Growth Hacking Professional Certificate, </strong>which is<strong> </strong>designed to give you the job-ready skills you need to run successful digital marketing campaigns and growth hacking initiatives. <br /> </div><div><strong>Course Syllabus</strong> </div><div><strong>Module 0: Welcome </strong> </div><ul><li>Course Introduction  </li><li>General Information </li><li>Objectives and Syllabus </li><li>Grading Scheme </li><li>How to Make the Most of This Course </li></ul><div><strong>Module 1: Fundamentals of Digital Advertising and Search Engine Marketing </strong> </div><ul><li>Module Introduction and Learning Objectives </li><li>Introduction to Digital Advertising  </li><li>Digital Advertising Terms  </li><li>Buying Models and Formats in Digital Advertising  </li><li>Digital Advertising Planning </li><li>Introduction to Growth Hacking in Digital Advertising </li><li>Advertising Planning </li><li>Introduction to Search Engine Marketing  </li><li>Keyword Research  </li><li>Bidding Strategies </li><li>Creating Text Ads in Google Ads </li><li>Setting Up a Google Ads Search Campaign </li><li>Keyword Research and Ad Grouping </li><li>Setting up a Search Campaign on Google </li><li>Campaign Optimization Fundamentals  </li><li>Optimization Techniques  </li><li>Data Analysis for Growth Hacking </li><li>Campaign Optimization </li><li>Optimizing a Campaign </li><li>Module 1 Practice Quiz: Fundamentals of Digital Advertising and Search Engine Marketing  </li><li>Module 1 Summary: Fundamentals of Digital Advertising and Search Engine Marketing  </li><li>Module 1 Graded Quiz: Fundamentals of Digital Advertising and Search Engine Marketing  </li></ul><div><strong>Module 2: B2B Lead Generation and Facebook and Instagram Advertising </strong> </div><ul><li>Module Introduction and Learning Objectives </li><li>Introduction to B2B Advertising and LinkedIn Advertising Platform  </li><li>Introduction to Growth Hacking in B2B Marketing </li><li>Audience Identification on LinkedIn  </li><li>B2B Campaigns on LinkedIn </li><li>Setting Up Campaigns on LinkedIn </li><li>B2B Lead Generation </li><li>B2B Targeting: LinkedIn Audience Creation </li><li>Setting Up a Campaign on LinkedIn Ad Platform </li><li>Facebook and Instagram Advertising Platform  </li><li>Audience Identification on Facebook  </li><li>Advertising Formats on Meta  </li><li>Meta Advertising Platform </li><li>Audience Targeting for Meta  </li><li>Creating a Facebook Advertising Campaign </li><li>Optimization Fundamentals  </li><li>Optimization Techniques: Meta  </li><li>Search Campaign Optimization </li><li>Optimizing a Campaign Based on Data </li><li>Module 2 Practice Quiz: B2B Lead Generation and Facebook and Instagram Advertising </li><li>Module 2 Summary: B2B Lead Generation and Facebook and Instagram Advertising </li><li>Module 2 Graded Quiz: B2B Lead Generation and Facebook and Instagram Advertising<strong> </strong> </li></ul><div><strong>Module 3: Shopping Campaigns and Influencer Marketing </strong> </div><ul><li>Module Introduction and Learning Objectives </li><li>Introduction to Shopping Campaigns Using Google Merchant Center  </li><li>Google Shopping Campaigns  </li><li>Integrating Google Merchant Centre with Google Ads Campaign </li><li>Growth Hacking for Online Sales </li><li>Shopping Campaign Planning </li><li>Introduction to Influencer Marketing  </li><li>Influencer Identification and Evaluation  </li><li>Influencer Marketing Guide </li><li>How to Hack Influencer Marketing for Faster Growth </li><li>Influencer Evaluation and Selection </li><li>Introduction to ROI Metrics  </li><li>Shopping Campaign Optimization  </li><li>Metrics and Analysis Guide </li><li>Shopping Campaign Optimization </li><li>Module 3 Practice Quiz: Shopping Campaigns and Influencer Marketing </li><li>Module 3 Summary: Shopping Campaigns and Influencer Marketing </li><li>Module 3 Graded Quiz: Shopping Campaigns and Influencer Marketing  </li></ul><div><strong>Module 4: Final Exam </strong> </div><ul><li>Module Introduction and Learning Objectives </li><li>Evolution of Digital Advertising </li><li>Final Exam: Digital Advertising  </li><li>Course Wrap-Up  </li><li>Video: Course Wrap-Up  </li><li>Course Glossary: Digital Advertising </li><li>Congratulations and Next Steps </li><li>Team and Acknowledgments </li><li>Course Rating and Feedback </li><li>Feedback </li></ul>
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Digital Marketing: Advertising

Master proven digital advertising strategies and techniques to run successful campaigns through Google Ads, social media, and AI marketing.  

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  <div>Want to master the art and science of social media marketing?    This course gives aspiring social media marketers, entrepreneurs, freelancers, and marketing professionals the highly sought-after skills to plan, create, and manage impactful social media campaigns that drive engagement and brand growth; critical capabilities for roles involving brand strategy, digital communications, campaign management, and content marketing.  </div><div> </div><div>After completing this course, you will be able to: </div><ul><li>Design and execute a complete social media strategy aligned with business goals. </li><li>Create compelling text, image, and video content that resonates with target audiences. </li><li>Analyze performance metrics to optimize social media engagement and ROI. </li><li>Use scheduling and management tools to maintain brand consistency and efficiency.  </li></ul><div><strong>Course Overview</strong> </div><div> </div><div>During this course, you'll learn to plan, create, manage, and evaluate social media activities across platforms such as Facebook, Instagram, LinkedIn, X (Twitter), and TikTok. </div><div>You'll look at the <strong>core pillars of social media marketing</strong>—from planning and strategy to content creation, calendarization, and analytics. You'll explore how to design effective social media strategies and choose the right platforms for your audience. Plus, you'll learn valuable techniques for crafting high-performing posts, leveraging user-generated content (UGC), and optimizing visual storytelling to enhance engagement. </div><div> </div><div>Throughout, you'll gain hands-on experience through guided labs and activities, including developing a social media strategy, creating engaging posts, building a content calendar, managing community interactions, and analyzing performance metrics using industry-standard tools. Then, in a  final project, you'll apply your knowledge managing a brand's social media presence end-to-end—demonstrating your readiness for real-world marketing roles. </div><div> </div><div>If you're looking to build a solid, demonstrable foundation in <strong>social media strategy, content creation, analytics, and community management</strong>, enroll today! </div><div><strong>Prerequisites</strong> </div><div>This is a beginner-friendly course. No prior experience in marketing or social media is required. Basic familiarity with using popular social media platforms is helpful but not mandatory. </div><div> </div><div><strong>Related Professional Certificates</strong> </div><div>This course is part of the <strong>IBM Digital Marketing Professional Certificate</strong> and the <strong>IBM Social Media Marketing Specialization</strong>, two programs that are designed to prepare you for roles such as <strong>Social Media Manager, Digital Marketing Specialist, Content Strategist,</strong> and <strong>Brand Engagement Lead</strong>. </div><div> </div><div><strong>Course Syllabus</strong> </div><ul><li><strong>Module 0: Welcome</strong> </li><li>Course Introduction, Syllabus, and Grading Scheme </li><li><strong>Module 1: Social Media Planning and Strategy</strong> </li><li>Fundamentals of Organic Social Media and Growth Hacking </li><li>The Social Media Planning Process and Platform Selection </li><li>Lab: Creating a Basic Social Media Plan </li><li><strong>Module 2: Content Creation and Calendarization</strong> </li><li>Crafting Text, Image, and Video Content </li><li>Leveraging Stories and User-Generated Content (UGC) </li><li>Lab: Creating and Optimizing Social Media Content </li><li>Building and Managing a Social Media Calendar </li><li><strong>Module 3: Social Media Management and Analytics</strong> </li><li>Community Management and Engagement Techniques </li><li>Understanding Social Media Metrics and Insights </li><li>Lab: Using Analytics and Management Tools Effectively </li><li><strong>Module 4: Assessment and Final Project</strong> </li><li>Final Project: Social Media Management for a Brand </li><li>Final Quiz and Course Wrap-Up </li></ul>
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Digital Marketing: Social Media Management

Master how to build social media strategies, create engaging content, and analyze performance to grow brand presence and boost engagement across key platforms. 

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  <div>In this guided project, you’ll learn how to build an <strong>AI-powered World Series Baseball Data Assistant</strong> using <strong>LangGraph </strong>and <strong>LangChain Pandas Agents.</strong> You’ll design a smart, two-step agentic workflow that (1) routes a natural-language question to the correct dataset (World Series, playoffs, regular season, team, batting, or pitching) and (2) queries that data with a ReAct-style Pandas agent to produce clear, human-readable answers.<br /><br /></div><div>This project is perfect for learners who want to go beyond simple prompts and understand how to create <strong>agentic AI systems</strong> that reason over <strong>structured data</strong>. You’ll gain practical experience connecting LLM reasoning, keyword routing, robust error handling, and state management inside a <strong>LangGraph</strong> workflow, skills you can reuse for sports analytics, finance dashboards, or any data-driven assistant.<br /><br /></div><div>By the end of this project, you will be able to:</div><ul><li>Understand the fundamentals of <strong>LangGraph</strong> and how it structures multi-step agentic workflows.</li><li>Build a compact <strong>route → query</strong> pipeline that translates natural-language questions into Pandas operations.</li><li>Create and configure <strong>Pandas DataFrame agents</strong> for multiple CSV datasets (World Series, playoffs, regular season, team, player batting, pitcher stats).</li><li>Implement a<strong> routing node</strong> that maps questions to the right dataset and a <strong>query node</strong> with guided retries and schema previews for stability.</li></ul><h1>What You'll Need</h1><div>This project is designed for <strong>beginners to intermediate learners</strong> with a basic understanding of Python. No prior experience with LangGraph or agentic AI is required, everything will be explained step by step.<br /><br /></div><div>You’ll need:</div><ul><li>Basic familiarity with <strong>Python programming</strong> and running Jupyter notebooks</li><li>An internet connection to install packages</li></ul><div>All other libraries will be installed automatically within the lab environment.</div>
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Build a Baseball Data Analysis Agent w/ LangGraph

Learn how to build an AI-powered baseball data analyst using LangGraph and Pandas that can analyze World Series statistics and answer questions using natural language. In this guided project, you’ll explore LangGraph and Pandas Agents to create a smart workflow that routes queries to the correct dataset, interprets structured data, and generates real-time insights. Perfect for beginners in data science, AI agents, or sports analytics looking to combine data reasoning with automation.

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  <div>Build the foundational knowledge and ethical awareness you need to start a career in ethical hacking and cybersecurity. This course is ideal for aspiring cybersecurity professionals who are keen to learn key ethical hacking principles to understand hacker mindsets, legal frameworks, and best practices for responsible cybersecurity testing. Organizations are actively seeking ethical hackers and security specialists who can safeguard data, systems, and networks against evolving threats. <br /><br />After completing this course, you will be able to: <br /><br /></div><ul><li>Explain key ethical hacking concepts and apply them responsibly to strengthen system security. </li><li>Identify different types of hackers, attack motivations, and hacking methodologies. </li><li>Understand cybersecurity laws, legal boundaries, and responsible disclosure processes. </li><li>Document and report security vulnerabilities effectively and ethically. </li></ul><div><br /></div><h1><strong>Course Overview </strong></h1><div>During the course, you’ll explore the core principles that guide ethical hacking, learning how hackers think and operate so you can anticipate and defend against cyber threats. You’ll begin by looking at hacker classifications, motivations, and studying real-world case studies that reveal how ethical hacking strengthens security resilience.  <br /><br />You’ll also examine the <strong>ethical, technical, and legal aspects of hacking</strong>, learning about <strong>global cybersecurity laws</strong>, ethical decision-making, and responsible disclosure practices that distinguish legitimate testing from illegal hacking.  <br /><br />Throughout, hands-on activities and podcasts will reinforce your learning as you identify hacker types, review ethical codes of conduct, analyze reflected XSS vulnerabilities, and simulate responsible disclosure reporting. And these exercises will then help you apply ethical hacking principles in realistic scenarios, preparing you for further technical training or certification pathways.<br /> <br />By the end of the course, you’ll have a solid understanding of the <strong>principles, laws, and professional ethics</strong> governing the cybersecurity landscape. You’ll be equipped to pursue advanced cybersecurity and ethical hacking certifications, strengthen your professional portfolio, and contribute to safer digital environments. <strong>Enroll now to start your journey toward becoming the responsible ethical hacker businesses are looking for.</strong> </div><div> </div><h1><strong>Prerequisites</strong> </h1><div>This is an <strong>introductory-level</strong> course designed for beginners. No prior hacking or cybersecurity experience is required. A basic understanding of computers and networks is helpful but not mandatory. </div><div> </div><h1><strong>Related Professional Certificates</strong> </h1><div>This course is part of the <strong>IBM Cybersecurity Analyst Professional Certificate</strong> and complements other learning paths such as <strong>Cybersecurity Fundamentals</strong> and <strong>Penetration Testing Essentials</strong>. Completing these programs prepares you for entry-level roles such as <strong>Cybersecurity Analyst, Ethical Hacking Assistant,</strong> or <strong>Information Security Associate</strong>. </div><div> </div><h1><strong>Course Syllabus</strong> </h1><ul><li><strong>Welcome to the Course</strong> <ul><li>Course Introduction, Overview, and Learning Objectives </li></ul></li><li><strong>Module 1: Ethical Hacking Principles and Practices</strong> <ul><li>Ethical Hacking Concepts, Hacker Motivations, and Classifications </li><li>Real-world Ethical Hacking Cases and Code of Conduct </li><li>Activities and Podcasts on Hacker Types and Ethics </li><li>Practice and Graded Quizzes on Ethical Hacking Fundamentals </li></ul></li><li><strong>Module 2: Cybersecurity Laws and Legal Boundaries</strong> <ul><li>Overview of Global Cybersecurity Laws and Legal Pitfalls </li><li>Ethical versus Illegal Hacking and Responsible Disclosure </li><li>Bug Bounty Programs and Security Report Writing </li><li>Activities and Podcasts on Legal Boundaries and Vulnerability Reporting </li><li>Practice and Graded Quizzes on Cybersecurity Ethics and Laws </li></ul></li><li><strong>Module 3: Course Wrap-Up</strong> <ul><li>Course Summary, Glossary, and Next Steps </li></ul></li><li><strong>Module 4: Course Rating and Feedback</strong> </li></ul>
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Ethical Hacking Fundamentals

Learn essential ethical hacking principles, laws, and responsible disclosure practices to protect systems and build a career in cybersecurity. 

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  <div>Are you ready to build AI that thinks, acts, and gets things done? In this course, you’ll learn how to design agents that go beyond language generation to reason, take action, and tackle real-world tasks using tools and data.  </div><div> </div><div>During the course, you'll explore the foundations of function calling, tool orchestration, and chaining with LangChain. You’ll discover how to extend the capabilities of Large Language Models (LLMs) by connecting them with calculators, code, and external data sources. You'll learn how LLMs trigger tool use through LangChain Expression Language (LCEL) and look at manual tool calling for greater control and accuracy. Plus, you’ll explore built-in agents that can analyze data, create visualizations, and run SQL queries using natural language.  </div><div> </div><div>To get the most from this course, we recommend that you have Python programming skills, a basic understanding of LangChain, and familiarity with core AI concepts.  </div><div> </div><div>Whether you're building a chatbot or a smart assistant, if you’re looking to build the skills to create dynamic, intelligent, and goal-oriented AI systems, enroll today!  </div><div><br /><br /></div><h1>Learning Objectives</h1><ul><li>Develop AI agents that can reason and perform tasks independently  </li><li>Implement tool calling and chaining to create structured AI workflows </li><li>Utilize built-in LangChain agents to analyze data, generate visualizations, and execute database queries  </li><li>Apply best practices in prompt engineering and tool calling to enhance AI agent performance</li></ul><div> </div><h1>Course Syllabus</h1><div><strong><br />Module 1: Foundations of Tool Calling and Chaining<br /></strong><br /></div><ul><li>Lesson 0: Welcome</li><li>Lesson 1: Introduction to AI Agents</li><li>Lesson 2:  Getting Started with Tool Calling </li><li>Lesson 3: Building and Orchestrating Tools</li><li>Lesson 4: Module Summary and Evaluation  </li></ul><div><br /></div><div><strong>Module 2: </strong> LCEL and Manual Tool Calling in LangChain  <br /><br /></div><ul><li>Lesson 1: Introduction to Chaining and LCEL Basics</li><li>Lesson 2: Manual Tool Calling Basics</li><li>Lesson 3: Parsing and Validating Tool Calls  </li><li>Lesson 4: Module Summary and Evaluation  </li></ul><div><br /></div><div><strong>Module 3: Using Built-in Agents in LangChain</strong> <br /><br /></div><ul><li>Lesson 1: Natural Language Data Visualization </li><li>Lesson 2: Conversational Database Access</li><li>Lesson 3: Module Summary and Evaluation</li><li>Lesson 4: Course Wrap-Up</li></ul><div><br /></div>
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Fundamentals of Building AI Agents

Learn to enhance AI workflows with function calling and tool integration in LangChain. This course covers the fundamentals of function calling, tool creation, and chaining techniques to streamline interactions with language models. You’ll start by building AI agents that dynamically select and execute tools, then explore chaining methods using LangChain Expression Language (LCEL) to link prompts, models, and outputs. Finally, you'll implement manual tool-calling pipelines for greater control, ensuring accuracy and reliability in high-stakes applications. 

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  <div>As AI models become more powerful, the question shifts from "how do we train better models?" to "how do we get more from the models we already have?" Test-time compute—the practice of spending additional computational resources during inference to improve output quality—has emerged as a critical technique for enhancing model performance without retraining. This guided project explores how strategic reasoning methods like Chain-of-Thought prompting, Best-of-N sampling, and self-verification can transform a model's ability to solve complex problems. <br /><br /></div><h1>What You'll Learn </h1><div>By the end of this project, you will be able to:</div><ul><li><strong>Explain the concept of Test-Time Compute</strong> and its role in improving model performance during inference without additional training or fine-tuning.</li><li><strong>Apply Chain-of-Thought prompting</strong> to enable step-by-step reasoning in large language models, making their problem-solving process transparent and more reliable.</li><li><strong>Use Best-of-N sampling</strong> to generate multiple candidate outputs and select the most accurate one through systematic evaluation and comparison.</li><li><strong>Implement self-verification strategies</strong> to allow models to check and refine their own answers, creating feedback loops that improve consistency.</li><li><strong>Analyze how increasing test-time compute affects solution accuracy, consistency, and efficiency</strong>, learning when these techniques provide meaningful improvements versus diminishing returns.</li></ul><h1><strong>Who Should Enroll</strong></h1><ul><li><strong>ML engineers and AI practitioners</strong> who want to optimize model performance at inference time and understand the reasoning strategies behind recent AI breakthroughs.</li><li><strong>Developers</strong> working with LLMs who need practical techniques to improve output quality and reliability without retraining or fine-tuning models.</li><li><strong>Data scientists</strong> interested in exploring how computational budgets at inference time can be strategically allocated to enhance reasoning and problem-solving capabilities.</li></ul><h1><strong>Why Enroll</strong></h1><div>This project gives you hands-on experience with the inference optimization techniques that are reshaping how we think about AI capabilities. Rather than accepting a model's first answer, you'll learn to implement systems that reason through problems systematically, explore alternative solutions, and verify their work. <br /><br /></div><h1><strong>What You'll Need</strong></h1><div>To get the most out of this project, you should have basic Python programming skills and some familiarity with working with language model APIs. Understanding of fundamental machine learning concepts is helpful but not required. All dependencies are pre-configured in the environment, and the project runs best on current versions of Chrome, Edge, Firefox, or Safari.</div>
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Solve Challenging Problems using Advanced Prompt Engineering

Learn how Chain-of-Thought prompting, Best-of-N Sampling, and Self Verification techniques trade compute for improved LLM accuracy on logical reasoning problems. This machine learning project teaches you to unlock better AI reasoning at inference time. By the end, you'll understand how to make models think harder when it matters most.

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  <h1>About this Course</h1><div>Chatbots can save businesses up to 30% on customer support costs alone (Shopify), and AI developers with strong chatbot skills are in high demand! This Building AI Powered Chatbots Without Programming course gives aspiring AI application developers and engineers the skills businesses need to build chatbots in just 6 weeks. No programming experience required! </div><div> </div><div><strong>You Will Learn:</strong>  </div><ul><li>The job-ready chatbot skills businesses need in just 6 weeks! No programming experience required. </li><li>How to embed an AI powered recommendation system into a chatbot to ensure personalized customer service.  </li><li>How to implement and test complex workflows within chatbot actions to enhance user interactions.  </li><li>Plus, you’ll gain hands-on experience deploying a chatbot in a real-world scenario </li></ul><div> </div><div><strong>Course Overview</strong> </div><div> </div><div>Designed for students or professionals interested in creating chatbots, this course equips learners with the skills to create and deploy effective chatbot solutions without any coding requirements. </div><div> </div><div>During this course, you’ll explore what chatbots are and how they benefit businesses and users. You’ll learn about action workflows and the key features of a chatbot. You’ll dive into the high-level differences between decision-tree chatbots and generative AI chatbots. Plus, you’ll look at the concept of actions within chatbots and learn to build them using IBM watsonx Assistant.  </div><div> </div><div>Through interactive labs, you’ll also gain valuable hands-on experience applying what you’ve learned to real-world scenarios, such as creating a customer care chatbot for a flower shop. Great for talking about in interviews! </div><div> </div><div>If you’re looking to develop the essential chatbot skills employers are looking for, enroll today and get ready to power up your resume in just 6 weeks! </div><div> </div><div><strong>Prerequisites</strong> </div><div> </div><div>This is a beginner-level course, so no prior knowledge or programming experience is required to take this course. </div><div> </div><div><strong>IBM Specialization</strong>   </div><div> </div><div>This course is part of the IBM AI Foundations for Everyone Specialization. If you’re keen to kickstart a career in AI, we recommend you enroll for the full Specialization program and work through the courses in order. Within just a few weeks, you’ll build job-ready AI skills to enhance your AI career options and have practical experience on your resume that will catch the eye of employers! </div>
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Building AI Powered Chatbots Without Programming

Get hands-on creating intelligent, business-ready, AI-powered chatbots without any programming experience.

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  <div>Gain job-ready UX/UI skills with hands-on experience in UX research and information architecture (IA). </div><div><br />This course equips aspiring designers and web developers with in-demand skills to create user-centered digital products. <br /><br /></div><div>You’ll build a strong foundation in UX research and learn how it integrates into the product lifecycle. Explore methods for conducting user interviews and surveys using tools like Google Docs and Forms,  and turn research data into insights with affinity mapping. Analyze findings to develop user personas and use a prioritization matrix to translate user needs into actionable features. <br /><br /></div><div>Dive into IA, a key element for efficient navigation and content organization, applying card sorting and tree testing techniques. Finally, bring your skills together in a real-world-inspired project that demonstrates your ability to implement UX research and IA, perfect for showcasing in your portfolio or interviews. <br /><br /></div><h1>Learning Objectives </h1><div>• Build hands-on experience in <strong>UX research</strong> and <strong>information architecture (IA)</strong> to feature in your portfolio and interviews. </div><div>• Learn UX research fundamentals, methods, and their role in the product development process. </div><div>• Create <strong>user personas</strong> using affinity mapping and priority matrices for new features. </div><div>• Develop effective <strong>IA</strong> with card sorting and tree testing to enhance navigation and usability. <br /><br /></div><h1>Course Syllabus</h1><div>Welcome to the Course </div><ul><li>Video: Course Introduction </li><li>Reading: Course Overview </li><li>Ungraded Pugin: Helpful Tips for Course Completion </li></ul><div>Module 1: Foundations of UX Research </div><ul><li>Learning Objectives </li></ul><div>Understanding UX Research </div><ul><li>Video: What is UX Research?  </li><li>Reading: How UX Insights Improve Business Outcomes </li><li>Video: UX Research vs. UX Design </li><li>Reading: Types of Research Methods in UX Design </li><li>Activity: When and Why to Conduct UX Research </li><li>Reading: Timing UX Research in Product Development </li><li>Lab: Getting Started with Miro </li><li>Lab: Mapping Methods to UX Design Stages </li><li>Practice Assignment: Understanding UX Research </li></ul><div>Research Methods in Practice </div><ul><li>Video: Conducting Effective User Interviews </li><li>Video: Designing Surveys with Google Forms </li><li>Lab: Design a Survey Using Google Forms </li><li>Video: Observational Studies &amp; Field Research </li><li>Reading: Writing Research Goals and Hypotheses </li><li>Reading: Field Study Best Practices </li><li>Practice Assignment: Research Methods in Practice  </li></ul><div>Summary and Assessment </div><ul><li>Module Summary: Foundations of UX Research </li><li>Graded Quiz: Foundations of UX Research </li></ul><div>Module 2: From Research to Insights </div><ul><li>Learning Objectives </li></ul><div>Synthesizing Research Data </div><ul><li>Video: Analyzing Open-Ended Survey Responses </li><li>Video: Affinity Mapping </li><li>Reading: Affinity Mapping Versus Theming </li><li>Lab: Getting started with Figma </li><li>Lab: Affinity Mapping Using FigJam </li><li>Video: Creating Personas from Real Data </li><li>Reading: Basics of Persona Creation </li><li>Practice Quiz: Synthesizing Research Data  </li></ul><div>From User Needs to Design Direction </div><ul><li>Video: Turning Research Into Features </li><li>Video: Mapping User Needs to Product Strategy </li><li>Reading: Feature Prioritization Matrix </li><li>Video: Presenting UX Findings to Teams </li><li>Reading: Writing Insight-Driven UX Recommendations </li><li>Lab: Affinity to Action: Prioritizing Features </li><li>Practice Assigment: From User Needs to Design Direction </li></ul><div>Summary and Assessment </div><ul><li>Module Summary: From Research to Insights </li><li>Graded Quiz: From Research to Insights </li></ul><div>Module 3: Designing Information Architecture </div><ul><li>Learning Objectives  </li></ul><div>Organizing Content and Navigation </div><ul><li>Video: What is Information Architecture? </li><li>Video: Flat versus Hierarchical Structures </li><li>Reading: Common IA Mistakes and Mental Models in IA Design </li><li>Video: Card Sorting in Information Architecture </li><li>Reading: Navigation Patterns in Modern Apps </li><li>Lab: Sorting the Menu Using a Card Sorting Technique </li><li>Lab: Building a Preliminary Sitemap </li><li>Practice Assignment: Organizing Content and Navigation  </li></ul><div>Testing and Finalizing IA </div><ul><li>Video: How to Build a Sitemap </li><li>Video: Tree Testing Navigation </li><li>Reading: Analyzing Tree Testing Results: Step-by-Step </li><li>Lab: Validating Sitemap Using Tree Testing </li><li>Video: Real-World IA Redesign Example: Travel Booking Website </li><li>Reading: How to Label Navigation for Clarity and Consistency </li><li>Lab: Refining BlossomRoot a Flower &amp; Plant Store Sitemap </li><li>Practice Assignment: Testing and Finalizing IA  </li></ul><div>Summary and Assessment </div><ul><li>Module Summary: Designing Information Architecture </li><li>Graded Quiz: Designing Information Architecture  </li></ul><div>Module 4: Assessment and Final Project </div><ul><li>Learning Objectives </li></ul><div>Final Project/Course Summary and Final Quiz </div><ul><li>Reading: Project Overview </li><li>Final Project: Redesign UX Research Driven Travel Planning Application </li><li>Final Submission and Evaluation  </li></ul><div>Final Assessment and Course Wrap-Up  </div><ul><li>Video: Course Wrap-Up </li><li>Course Glossary: UX Research and Information Architecture </li><li>Final Exam: UX Research and Information Architecture </li><li>Reading: Congratulations and Next Steps </li><li>Reading: Team and Acknowledgments</li></ul><h1>General Information</h1><div>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari. <br /><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>No college degree or programming experience is required. Having basic computer literacy and a background in designing is advantageous. </div>
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UI/UX Design: Research and Information Architecture

Gain UX/UI design skills with hands-on experience in UX research and information architecture (IA). Conduct user research, create personas, design IA, and complete a portfolio-ready project.

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  <div>Showcase your systems analysis expertise in this portfolio-ready capstone course by applying your skills to design and evaluate real-world business systems. <br /><br /></div><div>In this hands-on project, you’ll streamline business processes, manage data, and integrate systems using proven systems analysis methodologies. Gather requirements, create process models with UML diagrams, design relational databases, and build interactive dashboards with Tableau or Cognos. <br /><br /></div><div>Demonstrate end-to-end skills in stakeholder management, data security, and compliance while applying techniques for business requirements documentation, process modeling, and deployment with CI/CD pipelines. <br /><br /></div><div>By the end of this course, you’ll have a comprehensive capstone project showcasing your ability to tackle real organizational challenges and improve business processes—experience you can highlight in interviews. <br /><br /></div><div>Enroll now to gain practical, career-ready experience as a systems analyst!<br /><br /></div><h1>Learning Objectives</h1><div>• Showcase practical systems analysis skills to design IT solutions that improve business operations and data management. </div><div>• Develop process models, entity-relationship diagrams, and dashboards to visualize and optimize workflows. </div><div>• Apply data quality, security, and compliance practices following industry standards. </div><div>• Create end-to-end IT solutions that meet stakeholder requirements and enable data-driven decision-making. <br /><br /></div><h1>Course Syllabus</h1><div>Welcome to the Course </div><ul><li>Video: Course Introduction </li><li>Reading: Course Overview </li><li>Video: Project Overview </li><li>Reading: Helpful Tips for Course Completion </li></ul><div>Module 1: Project Initiation and Requirements Gathering </div><ul><li>Learning Objectives </li></ul><div>Lesson 1: Project Initiation and Stakeholder Engagement </div><ul><li>Reading: Assignment Overview: Project Initiation and Stakeholder Engagement </li><li>Lab: Project Charter, Stakeholder Register, and Stakeholder Engagement Plan </li><li>Graded Assignment: Checklist: Project Initiation and Stakeholder Engagement </li></ul><div>Lesson 2: Requirements Gathering </div><ul><li>Reading: Assignment Overview: Requirements Gathering </li><li>Lab: Requirements Gathering </li><li>Graded Assignment: Checklist: Requirements Gathering </li></ul><div>Lesson 3: Summary and Assessment </div><ul><li>Reading: Summary and Highlights: Project Initiation and Requirements Gathering </li><li>Graded Quiz: Project Initiation and Requirements Gathering </li></ul><div>Module 2: Project Planning and Process Modeling </div><ul><li>Learning Objectives </li></ul><div>Lesson 1: Project Planning and Risk Analysis </div><ul><li>Reading: Assignment Overview: Project Planning and Risk Analysis </li><li>Lab: Create Project Management and Risk Artifacts </li><li>Graded Assignment: Checklist: Project Planning and Risk Analysis </li></ul><div>Lesson 2: Process Modeling </div><ul><li>Reading: Assignment Overview: Process Modeling and Visualization </li><li>Lab: Create Process Models and Visualization </li><li>Graded Assignment: Checklist: Process Modeling </li></ul><div>Lesson 3: Summary and Highlights </div><ul><li>Reading: Summary and Highlights: Project Planning and Process Modeling </li><li>Graded Quiz: Project Planning and Process Modeling </li></ul><div>Module 3: Data Preparation and Visualization </div><ul><li>Learning Objectives </li></ul><div>Lesson 1: Data Preparation and Cleaning </div><ul><li>Reading: Assignment Overview: Data Preparation and Cleaning </li><li>Lab: Analyze Business Needs and Prepare Patient Data </li><li>Graded Assignment: Checklist: Data Preparation and Cleaning </li></ul><div>Lesson 2: Basic Visualizations </div><ul><li>Reading: Assignment Overview: Basic Visualizations </li><li>Lab: Building Visualizations for Appointment and Billing Data </li><li>Graded Assignment: Checklist: Basic Visualizations </li></ul><div>Lesson 3: Advanced Dashboard Development </div><ul><li>Reading: Assignment Overview: Advanced Dashboard Development </li><li>Lab: Build Interactive Dashboards </li><li>Graded Assignment: Checklist: Advanced Dashboard Development </li></ul><div>Lesson 4: Summary and Highlights </div><ul><li>Reading: Summary and Highlights: Data Preparation and Visualization </li><li>Graded Quiz: Data Preparation and Visualization </li></ul><div>Module 4: System and Database Design </div><ul><li>Learning Objectives </li></ul><div>Lesson 1: System Design </div><ul><li>Reading: Assignment Overview: System Design with Use Case and Sequence Diagrams </li><li>Lab: Create System Models and UML Diagrams </li><li>Graded Assignment: Checklist: System Design </li></ul><div>Lesson 2: Database Design </div><ul><li>Reading: Assignment Overview: Database Design </li><li>Lab: Database Design </li><li>Graded Assignment: Checklist: Database Design </li></ul><div>Lesson 3: Summary and Highlights </div><ul><li>Reading: Summary and Highlights: System and Database Design </li><li>Graded Quiz: System and Database Design </li></ul><div>Module 5: System Testing and Deployment Strategies </div><ul><li>Learning Objectives </li></ul><div>Lesson 1: Test Planning and Case Design using TDD and BDD </div><ul><li>Reading: Assignment Overview: Test Planning and Case Design using TDD and BDD </li><li>Lab: Reviewing Test Plans and Creating TDD/BDD Test Cases </li><li>Graded Assignment: Checklist: Test Planning and Case Design using TDD and BDD </li></ul><div>Lesson 2: CI/CD Deployment and Monitoring Plan </div><ul><li>Reading: Assignment Overview: CI/CD Deployment and Monitoring Plan </li><li>Lab: Create a CI/CD Pipeline and Monitoring Strategy </li><li>Graded Assignment: Checklist: CI/CD Deployment and Monitoring Plan </li></ul><div>Lesson 3: Summary and Highlights </div><ul><li>Reading: Summary and Highlights: System Testing and Deployment Strategies </li><li>Graded Quiz: System Testing and Deployment Strategies </li></ul><div>Module 6: Final Submission, Evaluation, and Career Guidance </div><ul><li>Learning Objectives </li></ul><div>Lesson 1: Presentation Development, Submission, and Evaluation </div><ul><li>Reading: Assignment Overview: How to Present Your Findings </li><li>Lab: Unified IT Solution for XYZ Health Solutions </li><li>Peer Review: Project: Final Submission and Evaluation </li></ul><div>[Bonus] Lesson 2:  Building Your Resume and Portfolio </div><ul><li>Video: Guide to Resume Writing </li><li>Lab: Drafting System Analyst Resume </li><li>Video: Guide to Developing a Personal Portfolio </li><li>Lab: Developing a Personal Portfolio </li></ul><div>[Bonus] Lesson 3: Exploring Career Paths and Interview Preparation </div><ul><li>Video: Guide to Freelancing and Job Search </li><li>Lab: Planning a Job Search and Freelance Strategy </li><li>Video: Common Interview Questions and Tips </li><li>Activity: Practice Mock Interview </li></ul><div>Lesson 4: Course Wrap-Up </div><ul><li>Reading: Congratulations and Next Steps </li><li>Reading: Team and Acknowledgements </li></ul><h1>General Information</h1><div>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari. <br /><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>The following skills are required to be successful with this course:  </div><ul><li>We recommend completing all previous courses in the IBM Systems Analyst Professional Certificate before starting this capstone. </li></ul>
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Capstone Project: Systems Analyst

Demonstrate systems analysis and data management skills in this hands-on capstone. Design business systems, UML models, dashboards, and relational databases, and showcase a portfolio-ready project.

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  <div>Over 70% of software issues are discovered post-release, creating high demand for professionals skilled in testing, deployment, and system maintenance. This course equips systems analysts and architects to ensure reliable, production-ready systems.  <br /><br /></div><div>Learn to create test plans aligned with business objectives and technical requirements, track testing progress with traceability, and assess system readiness for release. Gain hands-on experience coordinating user acceptance testing, developing deployment checklists, and establishing processes that simplify launches. </div><div><br />Through practical exercises, tackle real-world deployment challenges, apply data validation techniques such as dry runs and record counts, and manage risk through structured planning and stakeholder communication. </div><div><br />By mastering testing and release frameworks, you’ll be prepared to lead teams through smooth go-lives and maintain long-term system stability. </div><div><br />Enhance your systems analysis and deployment skills and build the confidence to manage the critical final stages of the software lifecycle—enroll now! <br /><br /></div><h1>Learning Objectives</h1><div>• Plan and document testing processes, including traceability matrices, while collaborating with QA teams, developers, and key stakeholders. </div><div>• Prepare comprehensive deployment checklists detailing launch steps, go/no-go criteria, rollback strategies, and data validation methods. </div><div>• Establish critical system metrics to monitor performance post-launch, develop response plans, and drive improvements through retrospectives. </div><div>• Create a complete Testing and Release Kit featuring test strategies, validation checklists, and post-launch support to ensure smooth implementation. </div><div><br /></div><h1>Course Syllabus</h1><div>Welcome </div><ul><li>Video: Course Introduction </li><li>Reading: Course Overview </li><li>Helpful Tips for Course Completion </li></ul><div>Module 1: Testing Strategies and Practices </div><ul><li>Learning Objectives </li></ul><div>Introduction to Software Testing </div><ul><li>Video: The Importance of Testing  </li><li>Video: Testing Levels and Release Cycle </li><li>Video: Key Roles and Responsibilities in Software Testing </li><li>Video: Testing Case Study </li><li>Practice Quiz: Introduction to Software Testing </li></ul><div>Test Planning and Design </div><ul><li>Video: Test Coverage </li><li>Video: Anatomy of a Test Case </li><li>Reading: Sample Test Plan with Analyst Comments </li><li>Video: Creating and Using a Traceability Matrix </li><li>Lab: Role Play Scenario: Test Planning and Design </li><li>Lab: Review and Annotate a Test Plan for a Login System </li><li>Practice Quiz: Test Planning and Design </li></ul><div>Implementing TDD and BDD </div><ul><li>Video: TDD and BDD </li><li>Video: Benefits of Test Driven Development </li><li>Video: Benefits of BDD </li><li>Video: BDD Workflow and Gherkin Syntax </li><li>Video: Exploring TDD and BDD Through Examples </li><li>Lab: Role Play Scenario: Analyst's Role in TDD and BDD </li><li>Lab: Create TDD and BDD test cases </li><li>Practice Quiz: Implementing TDD and BDD </li></ul><div>Module Summary, Glossary, and Assessments </div><ul><li>Podcast: Software Testing Insights </li><li>Reading: Summary and Highlights: Testing Strategies and Practices </li><li>Glossary: Testing Strategies and Practices </li><li>Graded Assignment: Testing Strategies and Practices </li></ul><div> </div><div>Module 2: Deployment, Release, and Maintenance Strategies </div><ul><li>Learning Objectives </li></ul><div>Deployment and Release Management </div><ul><li>Video: Deployment versus Release: Key Concepts and Responsibilities </li><li>Video: Enabling CI/CD Planning </li><li>Video: Release Checklist Template with Roles and Approvals </li><li>Video: Rollback Strategy Essentials </li><li>Lab: Role Play Scenario: Support the Release of Software Feature </li><li>Lab: Release Plan with CI/CD Workflows </li><li>Practice Quiz: Deployment and Release Management </li></ul><div>Data Migration and Validation </div><ul><li>Video: Data Migration Basics: Legacy to New Systems </li><li>Video: Data Mapping and Validation Techniques </li><li>Video: Data Mapping: Case Study </li><li>Lab: Create a Data Migration Readiness Checklist </li><li>Lab: Role Play Scenario: Data Migration Mapping and Validation </li><li>Practice Quiz: Data Migration and Validation </li></ul><div>Post-Deployment and Continuous Improvement </div><ul><li>Video: Monitoring and Alerting: What to Track and Why </li><li>Video: Creating a Maintenance Calendar </li><li>Reading: Retrospectives and Post-Mortem Templates </li><li>Lab: Design a Monitoring Plan and Maintenance Checklist </li><li>Lab: Role Play Scenario: Post Deployment Incident Response </li><li>Practice Quiz: Post-Deployment and Continuous Improvement </li></ul><div>Module Summary, Glossary, and Assessments </div><ul><li>Podcast: Strategies for Software Deployment, Release and Maintenance </li><li>Reading: Summary and Highlights: Deployment, Release, and Maintenance Strategies </li><li>Glossary: Deployment, Release, and Maintenance Strategies </li><li>Graded Assessment: Deployment, Release, and Maintenance Strategies </li></ul><div> </div><div>Module 3: Final Project, Final Exam, and Course Wrap-Up </div><ul><li>Learning Objectives </li></ul><div>Project Planning and Design </div><ul><li>Reading: Final Project Overview </li><li>Video: Capstone Overview and Walkthrough </li><li>Reading: Planning Templates: Testing, Migration, Deployment </li><li>Final Project: Testing and Release Planning Kit </li><li>Final Project: Submission and Evaluation </li><li>Graded Assignment: Project Quiz: Testing and Release Planning Kit </li></ul><div>Final Exam and Course Wrap-Up </div><ul><li>Course Glossary: Software Testing, Deployment, and Maintenance Strategies </li><li>Graded Quiz: Final Exam </li><li>Video: Course Wrap-Up </li><li>Reading: Congratulations and Next Steps </li><li>Reading: Thanks from the Course Team </li></ul><h1>General Information</h1><div>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari.<br /><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>Learners who have a basic understanding of the software development process and want to enhance their skills in software maintenance and deployment.</div>
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Deploying, Testing and Maintaining Software Systems

Master systems analysis, software deployment, testing, and deployment skills. Learn to design and manage test plans, validate data, and lead smooth, reliable software releases.

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  <div>The need for Systems Analysts continues to grow rapidly (US Bureau of Labor Statistics), offering exciting opportunities for those entering the field. </div><div><br />This course is designed for business graduates and non-technical professionals seeking a practical introduction to systems analysis. You’ll gain a clear understanding of the analyst’s role, responsibilities, and the essential skills needed to succeed. </div><div><br />Explore foundational topics including the System Development Life Cycle (SDLC), requirements gathering, process modeling, and feasibility studies. Dive into methodologies like Agile and Waterfall, and learn key tools for documenting and designing systems.  </div><div><br />You’ll practice identifying system problems, evaluating their impact on business operations, and applying critical thinking, problem-solving, and communication skills to bridge the gap between business and technology.  <br /><br /></div><div>Interactive learning includes podcasts and AI-driven role-play exercises to make your experience engaging and practical. <br /><br /></div><div>Start your journey toward becoming a confident, in-demand <strong>Systems Analyst</strong> and build the expertise employers value—enroll today! <br /><br /></div><h1>Learning Objectives</h1><div>• Explore how a Systems Analyst drives success in Business Information Systems (BIS) by bridging technology and business needs. </div><div>• Gain essential knowledge of Systems Analysis to pinpoint organizational requirements and opportunities. </div><div>• Understand the core concepts and stages of the System Development Life Cycle (SDLC). </div><div>• Analyze system problems and understand their effects on business operations.<br /><br /></div><h1>Course Syllabus</h1><div>Welcome to the Course </div><ul><li>Video: Overview of IBM Systems Analyst Professional Certificate </li><li>Reading: Course Overview </li><li>Helpful Tips for Course Completion </li></ul><div>Module 1: Introduction to Systems Analysis <br /><br /></div><div>Learning Objectives </div><div>What is a System? </div><ul><li>Video: What is a System? </li><li>Video: Systems Fundamentals </li><li>Video: Expert Viewpoint: What is Systems Analysis? </li><li>Reading: Summary and Highlights: What is a System? </li><li>Practice Quiz: What is a System? </li></ul><div>Understanding Systems and the Analyst’s Role in Business </div><ul><li>Video: Types of Systems </li><li>Video: Introduction to SDLC </li><li>Video: Identifying System Issues and Business Needs </li><li>Activity: Optimize Brew Haven's Ordering System </li><li>Video: Expert Viewpoint: A Systems Analyst’s Role in Business Success </li><li>Reading: Summary and Highlights: Understanding Systems and the Analyst’s Role in Business </li><li>Practice Quiz: Understanding Systems and the Analyst’s Role in Business </li></ul><div>Glossary and Assessment </div><ul><li>Reading: Glossary: Introduction to Systems Analysis </li><li>Graded Quiz: Introduction to Systems Analysis </li><li>Discussion Prompt: Meet and Greet </li></ul><div> </div><div>Module 2: The Systems Analyst </div><div>Learning Objectives </div><div>Introduction to Systems Analyst </div><ul><li>Video: Introduction to Systems Analyst </li><li>Video: Skills and Responsibilities of a Systems Analyst </li><li>Video: Systems Analyst’s Role in Digital Transformation </li><li>Reading: Comparing the Roles of Systems Analysts and Business Analysts </li><li>Video: Expert Viewpoint: Day in the Life of a Systems Analyst </li><li>Reading: Roles Complementary to a Systems Analyst </li><li>Video: Expert Viewpoint: Systems Analyst’s Role in Enterprise </li><li>Reading: Evolution and Importance of the Systems Analyst </li><li>Storyline: Diagnose and Fix Operational Issues </li><li>Reading: Deliverables of Systems Analysis Projects </li><li>Reading: Summary and Highlights: Introduction to Systems Analyst </li><li>Practice Quiz: Introduction to Systems Analyst </li></ul><div>Career in Systems Analysis </div><ul><li>Reading: Job Description </li><li>Video: How to Become a Systems Analyst </li><li>Reading: Formal Organization Structure </li><li>Video: Expert Viewpoint: Preparing for a Career in Systems Analysis </li><li>Reading: Certifications in Systems Analysis </li><li>Video: Career Path – Where Can You Start and End Up as a Systems Analyst? </li><li>Reading: The Future of the Systems Analyst Role </li><li>Video: Building a Professional Network as a Systems Analyst </li><li>Video: Expert Viewpoint: Overcoming Challenges as a Systems Analyst </li><li>Storyline: Navigating Your First Systems Analyst Role </li><li>Reading: Summary and Highlights: Career in Systems Analysis </li><li>Practice Quiz: Career in Systems Analysis </li></ul><div>Glossary and Assessment </div><ul><li>Reading: Glossary: The Systems Analyst </li><li>Graded Quiz: The Systems Analyst </li><li>Discussion Prompt: What excites you about the role of Systems Analyst? </li></ul><div> </div><div>Module 3: Tools Used by Systems Analysts </div><ul><li>Learning Objectives  </li></ul><div>Tools Used by Systems Analysts  </div><ul><li>Video: Tools of Systems Analyst </li><li>Video: Data Analysis Tools for Systems Analysts </li><li>Video: Project Management and Documentation Tools for Systems Analysts </li><li>Video: Expert Viewpoint: What are the tools you use as a Systems Analyst? </li><li>Reading: Summary and Highlights: Tools of Systems Analyst </li><li>Practice Quiz: Tools of Systems Analyst </li></ul><div>Applying Systems Analyst Tools in Practice </div><ul><li>Video: Collaboration Tools for Effective Systems Analysis </li><li>Video: Prototyping and Simulation Tools </li><li>Video: Diagramming Tools for Systems Analysis </li><li>Lab: Getting Started with draw.io for Systems Modeling </li><li>Video: Expert Viewpoint: Real-World Impact of Systems Analyst Tools in Solving Business Problems </li><li>Storyline: Solving a Retail Systems Bottleneck with Analyst Tools </li><li>Reading: Summary and Highlights: Applying Systems Analyst Tools in Practice </li><li>Practice Quiz: Applying Systems Analyst Tools in Practice </li></ul><div>Glossary and Assessment </div><ul><li>Reading: Glossary: Tools Used by Systems Analysts </li><li>Graded Quiz: Tools Used by Systems Analysts </li><li>Discussion Prompt: Reflecting on what you've learned, which systems analyst tool (or category of tools) do you find most valuable, and why? </li></ul><div> </div><div>Module 4: Final Project and Course Wrap-Up </div><div>Learning Objectives </div><div>Final Project: Systems Analysis and Solution Design for a Real-World Business Problem </div><ul><li>Project Overview: Optimize the Inventory Management System for a Small Retail Store </li><li>Final Project - Part 1: Analyzing the current system </li><li>Final Project - Part 2: Designing the future system </li><li>Final Project Submission and Evaluation </li><li>Graded Assignment: Final Exam </li></ul><div>Course Wrap-Up </div><ul><li>Video: Course Wrap-Up </li><li>Video: Expert Viewpoint: Tips &amp; Best Practices for Starting a Career in System Analysis </li><li>Reading: Congratulations and Next Steps </li><li>Reading: Team and Acknowledgments </li></ul><div><br /></div><h1>General Information</h1><div>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari. <br /><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>You should have a basic computer literacy. It would be beneficial to have skills in analytical and logical thinking, problem solving and decision making. </div>
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Systems Analysis Fundamentals

Kickstart your career as a Systems Analyst. Learn about SDLC, requirements gathering, process modeling, Agile & Waterfall methods, and essential tools to bridge business needs and technical solutions. 

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  <div>Get ready for a hands-on learning experience where you’ll use practical tools and strategies to analyze, design, and evaluate IT systems! This course gives you in-demand IT systems design and analysis skills to advance your career in technology and business. </div><div> </div><div><strong><em>You will learn to:</em></strong>  </div><ul><li>Develop data flow diagrams, entity-relationship diagrams, and Unified Modeling Language (UML) diagrams to represent data structures, system architecture, and workflows </li><li>Analyze existing IT systems by evaluating architecture, functionality, performance, and user experience to identify gaps and recommend enhancements </li><li>Perform cost-benefit analyses, feasibility studies, and risk assessments to compare IT solution options and propose the most effective approach </li><li>Apply system analysis, modeling, and evaluation methods in a project to design a digital transformation strategy for a business </li></ul><div><strong>Course overview</strong> </div><div> </div><div>During this course, you’ll discover how to align technology with business goals. Plus, you’ll work on real-world projects using techniques such as data flow diagrams, entity-relationship diagrams, and Unified Modeling Language (UML) to visualize and enhance system performance. </div><div> </div><div>You’ll learn how system architecture, technical workflows, and enterprise tools such as ERP and CRM drive business operations. You’ll develop the skills to identify inefficiencies, assess potential solutions, and analyze feasibility and risk to guide smarter decision-making. And, by combining structured and object-oriented design techniques with business process modeling, you’ll discover how to transform user requirements into efficient, effective solutions. </div><div> </div><div>Whether you’re launching a career in systems analysis or advancing your IT expertise, this course gives you the knowledge and skills to play a vital role in creating scalable, user-centered technology solutions. <br />Enroll today and boost your resume with in-demand IT systems design and analysis skills that employers need! </div><div> </div><div><strong>Prerequisites</strong> </div><div>The course is designed for business analysts, process specialists, data analysts, project managers, product owners, and business professionals.  </div><ul><li>A basic understanding of IT concepts, such as databases, software development, or programming, is recommended.  </li><li>Familiarity with business processes, data modeling, or diagramming tools (e.g., flowcharts, ERDs, UML) is helpful but not required.  </li><li>While no advanced programming or prior system analysis experience is needed, exposure to IT systems or business operations is beneficial.  </li></ul><div><strong>Related</strong><em> </em><strong>Professional Certificate</strong> </div><div>This course is part of the <strong>IBM Systems Analyst Professional Certificate</strong>. If you’re keen to gain skills to evaluate, design, and improve computer systems and business processes, we recommend you enroll for the full Professional Certificate and work through the courses in order. Within just a few months, you’ll have job-ready skills and practical experience on your resume that will catch the eye of an employer! </div><div> </div><div><strong>Course syllabus</strong> </div><div>The course consists of the following modules </div><ul><li>System Designing and Modeling </li><li>IT Systems Analysis and Review </li><li>Solution Evaluation and Feasibility Analysis </li><li>Final Project </li></ul>
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Systems Design and Analysis

Build hands-on skills to analyze, design, and optimize IT systems using modeling tools and data diagrams to drive business efficiency and transformation. 

<div>Launch your career in the fast-growing field of backend development. This professional certificate program equips you with the essential skills to become an entry-level backend JavaScript developer in as little as four months—no prior degree or software engineering experience required.<br><br>In this Professional Certificate, you will:</div><ul><li>Describe the role of backend development in modern web applications, including the use of JavaScript technologies such as Node.js and Express.</li><li>Apply version control best practices by managing and collaborating on code using Git and GitHub repositories.&nbsp;</li><li>Develop scalable and modular back-end applications using Node.js, Express, and integration with RESTful APIs.</li><li>Integrate backend and client-side components through REST APIs.</li><li>Employ containerization and orchestration tools such as Docker and Kubernetes to deploy, manage, and scale applications.</li><li>Adopt Agile and DevOps practices to enhance continuous integration and deployment (CI/CD), and security in back-end development workflows.</li></ul><div><br><strong>Program Overview <br></strong><br>As businesses increasingly rely on digital platforms, the demand for backend developers continues to surge. Backend developers create server-side applications that power user experiences by connecting frontend interfaces to databases, APIs, and networking technologies.<br><br></div><div>By completing this program, you’ll gain hands-on experience with the back-end tools and technologies that drive modern web and mobile applications. You’ll build a strong foundation in back-end development and learn to architect modular, scalable systems that support dynamic user experiences.<br><br></div><div>Core technologies covered include HTML, CSS, JavaScript, Node.js, Express, Git and GitHub, SQL and NoSQL databases, Docker, Kubernetes, Microservices, and Serverless computing. Throughout the program, you’ll build a portfolio of real-world projects designed to prepare you for job interviews with confidence. Upon completion, you’ll earn a Professional Certificate and a digital badge from IBM, credentials recognized by employers worldwide.<br><br></div><div>In the final course, you’ll also receive expert guidance on job preparation, including resume writing, portfolio development, and interview strategies, along with access to exclusive career support resources to boost your job search.<br><br></div><div><strong>Hands-on Learning and Projects</strong></div><div>Throughout the program, you’ll apply your skills through practical labs and projects, including:</div><ul><li>Publishing open-source projects on GitHub.</li><li>Developing a travel recommendation website using JavaScript APIs.</li><li>Creating a server-side application to manage book reviews with Node.js and Express.</li><li>Developing microservices based applications and deploying on IBM Cloud with CI/CD and serverless computing.</li><li>Writing a backend app with MongoDB and RESTful APIs, then deploying it using Kubernetes.</li><li>Crafting your backend developer resume and professional portfolio.</li></ul><div><br><strong>Prerequisites<br></strong><br>The following skills are required to be successful with this course:&nbsp;</div><ul><li>&nbsp;Basic computer and IT literacy.</li></ul><div><br></div>

IBM JavaScript Backend Developer Professional Certificate

Start your career as a Backend JavaScript Developer! Learn Node.js, Express, MongoDB & more. Get job-ready with hands-on projects. No degree or experience needed.

<div>This Professional Certificate is designed to equip you with the job-ready skills and portfolio needed for an entry-level role in Data Warehouse Engineering and Business Intelligence (BI). Through a series of online courses, you’ll immerse yourself in the high-demand role of a Data Warehouse Engineer and gain practical experience with the tools, technologies, and processes required to design, deploy, operationalize, and manage Enterprise Data Warehouses (EDWs).<br><br>In this Professional Certificate, you will:</div><ul><li>Construct and run SQL queries to retrieve, update, and manipulate data across relational databases such as MySQL, PostgreSQL, and IBM Db2.</li><li>Deploy and manage relational database systems while applying best practices to secure, operationalize, monitor, and optimize performance.</li><li>Develop automated data pipelines for batch and streaming ETL processes using Bash shell scripts, Apache Airflow, and Apache Kafka.</li><li>Design data models and populate enterprise data warehouses that support large-scale, structured data storage and access.</li><li>Utilize Business Intelligence (BI) tools such as IBM Cognos Analytics to create dashboards and generate insights from warehouse data.</li><li>Apply Linux/Unix shell commands and scripting techniques to automate routine database administration and data engineering tasks.</li></ul><div><br><br></div><div>By the end of the program, you will be able to perform the core responsibilities of a data warehouse engineer. You’ll work with Relational Database Management Systems (RDBMS) and write SQL queries to retrieve, manipulate, and manage data. You’ll automate tasks using Linux/UNIX shell scripting and build data pipelines using industry-standard tools such as Apache Airflow and Apache Kafka for Extract, Transform, and Load (ETL) processes.<br><br>You will gain hands-on experience with provisioning and managing databases, designing data models, and implementing data warehouses. Additionally, you’ll use Business Intelligence tools to create dashboards and reports that deliver actionable insights from large volumes of data.<br><br></div><div>This program is open to all learners, whether or not you have a college degree, and does not require any prior programming or data engineering experience—just a willingness to learn and grow in the data field.</div><div><br>The following skills are required to be successful in this program:&nbsp;</div><ul><li>Basic computer and IT literacy.</li></ul><div><br></div><div><strong>Applied Learning Project</strong></div><ul><li>Each course features hands-on labs and real-world projects that reinforce the concepts and skills taught. By the end of the program, you will have designed, configured, queried, and maintained multiple databases, and built data pipelines using real-world tools to showcase your job-ready skills.</li><li>You will begin by provisioning a cloud-based database instance. From there, you’ll design databases using Entity-Relationship Diagrams (ERDs), and create objects such as tables and keys using MySQL, PostgreSQL, and IBM Db2.</li><li>You’ll become proficient in SQL by using commands like SELECT, INSERT, UPDATE, and DELETE, while also learning how to filter, sort, and aggregate data.&nbsp;</li><li>You’ll build Bash scripts with common Linux/Unix commands to automate tasks.</li><li>Next, you’ll design and implement batch and streaming ETL pipelines using Apache Airflow and Kafka.&nbsp;</li><li>Finally, you’ll apply your knowledge by creating a fully functional data warehouse and building BI dashboards to extract insights from your data.</li></ul>

IBM Data Warehouse Engineer Professional Certificate

Launch your career in Data Warehousing and Business Intelligence. Gain hands-on experience and the skills needed for an entry-level Data Warehousing role.

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  <h1>About this course</h1><div>Step into the role of a digital marketing professional with this capstone project that challenges you to design, execute, and present a full online marketing campaign for a realistic brand. Combining creativity, strategy, and analytics, this hands-on course transforms your learning into measurable results.<br /><br /></div><div>After completing this course, you will be able to: </div><ul><li>Design and execute a complete digital marketing campaign for a brand using data-driven, AI-enhanced strategies.</li><li>Create a campaign brief with defined goals, audience personas, and channel selection.</li><li>Develop SEO, content, paid advertising, and social media plans aligned with campaign objectives.</li><li>Build conversion-optimized product pages and email sequences to drive engagement and sales.</li><li>Analyze marketing data from SEO, social, and ad campaigns to improve ROI and growth outcomes.</li><li>Present and compile deliverables into a professional, portfolio-ready marketing presentation.</li></ul><div>You’ll begin by developing a comprehensive campaign strategy, defining goals, and identifying target audiences through demographic and psychographic insights. Next, you’ll design an SEO and content marketing plan that includes keyword research, blog strategies, and content calendars to drive organic growth.</div><div> </div><div>Then, you’ll move into paid advertising, where you’ll plan and execute targeted ad campaigns for platforms like Google and Meta, estimate budgets, and forecast KPIs. In the social media module, you’ll create an organic social media content plan featuring high-impact visuals, storytelling, and scheduling with tools like Canva.</div><div> </div><div>Building on these foundations, you’ll develop an e-commerce product page and personalized email marketing sequence to drive conversions, followed by data analysis for growth, where you’ll optimize campaign performance using SEO, ad, and social media data.</div><div> </div><div>Finally, you’ll compile all deliverables into a portfolio-ready presentation that demonstrates your analytical, creative, and technical marketing skills, plus you can receive career guidance on showcasing your portfolio, refining your LinkedIn profile, and navigating freelancing or job interviews.</div><div> </div><div>By the end, you’ll have the confidence, toolkit, and campaign portfolio to prove you’re ready for the next step in your digital marketing career.</div><div><br /></div><h1>General Information:</h1><ul><li>This course is self paced. </li><li>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.</li></ul><div><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>The following skills are required to be successful with this course: </div><ul><li>We recommend completing all previous courses in the <strong>IBM Digital Marketing and Growth Hacking Professional Certificate</strong> before starting this capstone.</li></ul><div><br /></div><h1><strong>Course Syllabus:</strong></h1><div><strong>Welcome to the Course</strong></div><ul><li>Course Introduction</li><li>Course Overview</li><li>Reading: Project Overview</li><li>Setting Campaign Goals and Objectives</li></ul><div><br /></div><div><strong>Module 1: Campaign Strategy and Audience Targeting</strong></div><ul><li>Module Summary and Learning Objectives</li><li>Module Overview: Campaign Strategy and Audience Targeting</li><li>Recap: SMART Marketing Goals</li><li>Reading: Campaign Strategy and Related Template</li><li>Lab: Define Business Goals and Write SMART Objectives</li><li>Assignment: Checklist – Setting Campaign Goals and Objectives</li></ul><div>Defining Target Audience and Channels</div><ul><li>Recap: Audience Segmentation</li><li>Reading: Choosing Channels Based on Campaign Objectives</li><li>Lab: Define Target Audience and Channel Justification</li><li>Assignment: Checklist – Defining Target Audience and Channels</li><li>Quiz: Campaign Strategy and Audience Targeting</li></ul><div><br /></div><div><strong>Module 2: SEO and Content Marketing Plan</strong></div><div><br />SEO Research and Planning</div><ul><li>Module Summary and Learning Objectives</li><li>Module 2 Overview: SEO and Content Marketing</li><li>Recap: SEO Basics and Tools</li><li>Reading: Guide to Using Keyword Research Tools</li><li>Lab: Perform Keyword Research and Write Meta Titles</li><li>Assignment: Checklist – SEO Research and Planning</li></ul><div>Content Calendar and Blog Outline</div><ul><li>Recap: Writing Content for SEO</li><li>Reading: Content Calendar Template</li><li>Lab: Plan a Content Calendar and Write a Blog</li><li>Assignment: Checklist – Content Calendar and Blog Outline</li><li>Quiz: SEO and Content Marketing Plan</li></ul><div><br /><strong>Module 3: Digital Advertising</strong></div><div><br />Campaign Setup and Ad Creation</div><ul><li>Module Summary and Learning Objectives</li><li>Module 3 Overview: Digital Advertising</li><li>Recap: SEM and Meta Ads</li><li>Reading: Sample Ad Briefs and Templates</li><li>Lab: Create Ad Copies for Google and Facebook</li><li>Assignment: Checklist – Campaign Setup and Ad Creation</li></ul><div>Targeting and Budgeting</div><ul><li>Recap: Targeting Audiences in Paid Ads</li><li>Reading: Budget Planning and KPI Forecasting</li><li>Lab: Create an Ad Targeting Plan and Estimate Budget</li><li>Assignment: Checklist – Targeting and Budgeting</li><li>Quiz: Digital Advertising</li></ul><div><br /></div><div><strong>Module 4: Social Media Content Plan<br /><br /></strong>Platform Selection and Content Planning</div><ul><li>Module Summary and Learning Objectives</li><li>Module 4 Overview: Social Media Content Plan</li><li>Recap: Social Media Strategy</li><li>Reading: Social Media Calendar Guide</li><li>Lab: Select Platform and Draft Post Ideas</li><li>Assignment: Checklist – Platform Selection and Content Planning</li></ul><div>Post Creation and Optimization</div><ul><li>Recap: Visual Design and Caption Tips</li><li>Reading: Post Template Library</li><li>Lab: Create Sample Posts</li><li>Assignment: Checklist – Post Creation and Optimization</li><li>Quiz: Social Media Content Plan</li></ul><div><strong>Module 5: E-Commerce and Email Marketing</strong></div><div><br />Product Page Design</div><ul><li>Module 5 Overview: E-Commerce and Email Marketing</li><li>Recap: Optimizing Product Listings</li><li>Reading: Product Page Template</li><li>Lab: Create a Mock Product Page</li><li>Assignment: Checklist – Product Page Design</li></ul><div>Email Campaign</div><ul><li>Recap: Writing Engaging Email Copy</li><li>Reading: Email Sequence Ideas</li><li>Lab: Write Welcome and Promotion Emails</li><li>Assignment: Checklist – Email Campaign</li><li>Quiz: E-Commerce and Email Marketing</li></ul><div><br /></div><div><strong>Module 6: Data Analysis for Growth</strong></div><div><br />SEO Optimization</div><ul><li>Module 6 Overview: Data Analysis for Growth</li><li>Recap: SEO Optimization Techniques</li><li>Reading: SEO Optimization Best Practices</li><li>Lab: Optimize SEO Data and Provide Recommendations</li><li>Assignment: Checklist – SEO Optimization</li></ul><div>Campaign Optimization</div><ul><li>Recap: Campaign Optimization</li><li>Reading: Campaign Optimization Best Practices</li><li>Lab: Optimize Campaign Data and Provide Recommendations</li><li>Assignment: Checklist – Campaign Optimization</li></ul><div>Social Media Optimization</div><ul><li>Recap: Social Media Optimization</li><li>Reading: Best Practices for Social Media Optimization</li><li>Lab: Optimize Social Data and Provide Recommendations</li><li>Assignment: Checklist – Social Media Optimization</li><li>Quiz: Data Analysis for Growth</li></ul><div><br /></div><div><strong>Module 7: Final Project, Course Wrap-Up, and Job Tips</strong></div><div><br />Final Presentation</div><ul><li>Structure of a Presentation</li><li>Best Practices for Presenting Your Findings</li><li>Lab: Preparing the Final Presentation</li></ul><div>Project Compilation and Submission</div><ul><li>Reading: Final Submission Guidelines and Deliverables</li><li>Assignment: Final Project Submission and Evaluation (AI or Peer Graded Options)</li></ul><div>Bonus: Job Hunting Tips for Digital Marketing</div><ul><li>Job Search Tips</li><li>Resume Writing and Updating Your LinkedIn Tips</li><li>Mock Interview Prep and Tips</li><li>Getting Successful as a Freelancer</li><li>Building Your Digital Portfolio</li></ul><div>Course Wrap-Up</div><ul><li>Course Wrap-Up</li><li>Congratulations and Next Steps</li><li>Thanks from the Course Team</li></ul><div><br /></div>
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Digital Marketing: Capstone Project

Apply your digital marketing and AI skills to design and execute a complete campaign. Build SEO, content, ads, social, and email strategies; analyze data; and create a portfolio-ready project that showcases job-ready marketing expertise.

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  <h1>About this course</h1><div>Search Engine Optimization (SEO) and content marketing skills are essential for marketers, entrepreneurs, and growth hackers. Whether you’re seeking to advance your marketing career, build brand visibility, or scale your business, this course equips you with the strategies, tools, and insights needed to achieve measurable growth.<br /><br /></div><div>After completing this course, you will be able to: </div><ul><li>Explain the principles of SEO, including ranking factors and the relationship between on-page, off-page, and technical SEO.</li><li>Apply keyword research, meta optimization, and content structuring techniques to improve search visibility.</li><li>Develop effective off-page SEO strategies, including backlink building and domain authority enhancement.</li><li>Implement technical SEO best practices to optimize website speed, mobile responsiveness, and indexability.</li><li>Analyze SEO data using Google Search Console, Google Analytics, and SEO plugins to inform marketing decisions.</li><li>Design SEO-driven content marketing strategies that combine growth hacking, AI optimization, and high-quality content creation.</li></ul><div>You’ll begin by exploring the foundations of SEO, learning key concepts, ranking factors, and the distinctions between on-page, off-page, content, and technical SEO. You’ll understand how search engines like Google crawl, index, and rank pages, and how to optimize elements like keyword research, meta descriptions, and page titles for better visibility. You’ll also be introduced to Artificial Intelligence Optimization (AIO) and Generative Engine Optimization (GEO) as new frontiers in digital search.</div><div> </div><div>You’ll focus on off-page SEO and link-building, mastering techniques to strengthen domain authority, leverage backlinks, and improve search performance through guest posting, outreach, and brand reputation signals. You’ll learn to apply growth hacking principles to link-building strategies and external SEO efforts.</div><div> </div><div>You’ll dive into technical SEO and analytics, covering topics like site speed, crawling, mobile responsiveness, and schema markup. You’ll gain hands-on experience with Google Search Console (GSC), Google Analytics (GA), and SEO plugins to audit websites, interpret performance data, and enhance visibility using structured data and AI-powered tools.</div><div> </div><div>You’ll explore content marketing as the creative core of SEO. You’ll learn how to craft engaging, shareable content, distribute it effectively, and build authority through consistent, optimized messaging. You’ll also discover how to apply growth hacking to content strategy to attract and retain audiences. The course concludes with a final project and assessment, where you’ll apply SEO and content strategies to a real-world scenario.</div><div> </div><div>By the end of this course, you’ll have the job-ready SEO and content marketing skills needed to drive traffic, boost rankings, and accelerate digital growth.<br /><br /></div><h1>General Information:</h1><ul><li>This course is self paced. </li><li>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.</li></ul><div><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>This course is suitable for anyone interested in starting a career in digital marketing. No specific prerequisites or prior experience are required to take this course, except basic computer literacy. <br /><br /></div><h1><strong>Course Syllabus:</strong></h1><div><strong>Welcome to the Course</strong></div><ul><li>Course Introduction</li><li>Course Overview</li><li>Reading: Helpful Tips for Course Completion</li></ul><div><br /><strong>Module 1: Introduction to SEO and On-Page Optimization</strong></div><ul><li>Module Summary and Learning Objectives</li></ul><div>Fundamentals of SEO</div><ul><li>Introduction to SEO</li><li>Reading: Introduction to Generative Engine Optimization and AI SEO</li><li>Components of SEO</li><li>Reading: Developing an SEO Strategy</li><li>Reading: Introduction to Growth Hacking in SEO</li><li>Activity: Identifying and Applying Key SEO Components</li><li>Reading: Emerging Trends in SEO</li><li>Practice Quiz: Fundamentals of SEO</li></ul><div>On-Page SEO</div><ul><li>Introduction to On-Page SEO</li><li>Components of On-Page SEO</li><li>Reading: Setting Up and Structuring On-Page SEO</li><li>Reading: Leveraging Tools for On-Page SEO</li><li>Lab: Create and Optimize Content for On-Page SEO</li><li>Practice Quiz: On-Page SEO</li></ul><div>Module Summary and Assessment</div><ul><li>Summary and Highlights: Introduction to SEO and On-Page Optimization</li><li>Graded Quiz: Introduction to SEO and On-Page Optimization</li></ul><div><br /></div><div><strong>Module 2: Off-Page SEO and Link-Building</strong></div><ul><li>Module Summary and Learning Objectives </li></ul><div>Off-Page SEO and Backlinking</div><ul><li>Introduction to Off-Page SEO</li><li>Domain Authority and Backlinking</li><li>Reading: Components of Off-Page Activity</li><li>Lab: Identifying Off-Page Activities and Backlink Opportunities</li><li>Practice Quiz: Basics of Off-Page SEO</li></ul><div>Link-Building Strategies</div><ul><li>Introduction to Link Building and Best Practices</li><li>Reading: Domain Authority and Page Authority</li><li>Lab: Considerations for Link Building</li><li>Reading: Link-Building Approaches</li><li>Reading: Growth Hacking in Link Building</li><li>Practice Quiz: Link-Building Strategies</li></ul><div>Module Summary and Assessment</div><ul><li>Summary and Highlights: Off-Page SEO and Link Building</li><li>Graded Quiz: Off-Page SEO and Link Building</li></ul><div><br /></div><div><strong>Module 3: Technical SEO and Analytics</strong></div><ul><li>Module Summary and Learning Objectives</li></ul><div>Technical SEO</div><ul><li>Introduction to Technical SEO</li><li>Reading: Technical SEO Audits</li><li>Lab: SEO Audit</li><li>Reading: Best Practices in Technical SEO</li><li>Practice Quiz: Technical SEO </li></ul><div>Schema Markup and Structured Data</div><ul><li>Introduction to Schema Markup</li><li>Reading: Leveraging Tools for Schema Markup and Data Structuring</li><li>Reading: Setting Up and Structuring Schema Markup</li><li>Lab: Optimize Rich Snippets and Schema Markups</li><li>Practice Quiz: Schema Markup and Structured Data</li></ul><div>SEO Analysis and Performance Tracking</div><ul><li>SEO Analytics and Performance Tracking</li><li>Reading: Google Search Console</li><li>SEO Performance Analysis</li><li>Reading: Data Analysis for SEO</li><li>Lab: SEO Data Analysis</li><li>Reading: Growth Hacking Insights for SEO</li><li>Practice Quiz: Content for SEO</li></ul><div><br /></div><div><strong>Module 4: Content Marketing</strong></div><ul><li>Module Summary and Learning Objectives</li></ul><div>Content Marketing Fundamentals</div><ul><li>Introduction to Content Marketing</li><li>Reading: Content Marketing Strategy</li><li>Content Distribution and Marketing</li><li>Reading: Content Distribution Approaches</li><li>Lab: Content Marketing Activity</li><li>Reading: Growth Hacking Content Marketing Approaches</li><li>Practice Quiz: Content Marketing</li></ul><div>Role of Content in SEO</div><ul><li>Key Considerations for Content in SEO</li><li>Content Planning for SEO</li><li>Reading: Leveraging Tools for Content</li><li>Reading: The Content Planning Process for SEO</li><li>Lab: Creating Content for SEO</li><li>Practice Quiz: Role of Content in SEO</li></ul><div>Integrated Content and SEO Strategy</div><ul><li>Integrated Approach to Content and SEO</li><li>Developing an Integrated Strategy</li><li>Reading: Plan Construct for an Integrated Approach</li><li>Reading: Crafting an Integrated Content and SEO Strategy</li><li>Lab: Developing a Strategic Outline</li><li>Practice Quiz: Integrated Content and SEO Strategy </li></ul><div>Module Summary and Assessment</div><ul><li>Summary and Highlights: Content Marketing</li><li>Graded Quiz: Content Marketing</li></ul><div><br /></div><div><strong>Module 5: Assessment and Final Project</strong></div><ul><li>Module Summary and Learning Objectives</li></ul><div>Final Project</div><ul><li>Reading: Project Overview</li><li>Final Project: Crafting a Portfolio-Ready SEO Content</li><li>Final Submission: Crafting a Portfolio-Ready SEO Content </li></ul><div>Glossary and Final Exam</div><ul><li>Course Glossary: Search Engine Optimization &amp; Content Marketing</li><li>Final Quiz: Search Engine Optimization and Content Marketing</li></ul><div>Course Wrap-Up</div><ul><li>Course Wrap-Up</li><li>Congratulations and Next Steps</li><li>Thanks from the Course Team</li></ul>
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Digital Marketing: SEO and Content Marketing

Build job-ready SEO and content marketing skills with real tools and techniques to grow visibility, rankings, and traffic using AI optimization and growth hacking strategies.

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  <h1>About this course</h1><div>Get started with digital marketing fundamentals skills and learn what it takes to design, launch, and optimize campaigns that drive measurable business growth. <br /><br />After completing this course, you will be able to: </div><ul><li>Explain the foundational principles, purpose, and evolution of digital marketing in modern business.</li><li>Identify and compare key digital channels (SEO, social media, paid media, email, content) and their strategic uses.</li><li>Analyze target audiences using demographic and psychographic insights to guide campaign development.</li><li>Design cohesive digital strategies that align with business goals and performance metrics.</li><li>Apply analytics tools and KPIs (traffic, conversions, ROI, engagement) to measure and optimize results.</li><li>Evaluate career pathways and opportunities to grow as a digital marketer, freelancer, or entrepreneur.</li></ul><div>As digital channels now account for nearly 80% of marketing budgets, the demand for skilled marketers and growth hackers has never been higher. Whether you’re an aspiring marketing professional, freelancer, or small business owner, this course is your first step to thrive in today’s digital landscape.</div><div><br /></div><div>You’ll begin by exploring the foundations of digital marketing, including its evolution, strategic importance, and the elements that make campaigns successful. You’ll learn how to connect marketing goals with overall business objectives, understand target audiences, and build coherent, data-driven strategies that deliver measurable results.<br /><br /></div><div>Next, you’ll examine the major digital marketing channels: social media, SEO, advertising platforms, email, and content marketing. You will discover how to use them strategically to reach, engage, and convert audiences. You’ll practice segmenting and analyzing markets through demographic and psychographic insights, learning to craft campaigns that resonate with specific customer profiles and brand goals.<br /><br /></div><div>You will learn about campaign metrics and KPIs, to understand how to track and evaluate marketing performance effectively. You will also explore platform-specific insights from tools like Google Analytics and Social media dashboards, enabling you to analyze and derive actionable conclusions from data.<br /><br /></div><div>This course will also help you understand the various job roles in digital marketing as well as the opportunities that exist for you as a small business owner or freelance marketing professional. <br /><br /></div><div>You’ll apply your learning in a course project, integrating strategy, channel selection, and analytics into a complete digital marketing plan. A graded assessment reinforces your ability to design and evaluate campaigns, while additional resources like podcasts, expert insights, and career tips, to help you explore opportunities in digital marketing, freelancing, and entrepreneurship.<br /><br /></div><div>This course combines strategic understanding with practical application, preparing you to step confidently into the world of digital marketing and growth hacking.</div><div><br /></div><h1>General Information:</h1><ul><li>This course is self paced. </li><li>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.</li></ul><div><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>This course is suitable for anyone interested in starting a career in digital marketing. No specific prerequisites or prior experience are required to take this course, other than basic computer literacy. </div><div><br /></div><h1><strong>Course Syllabus:</strong></h1><div><strong>Course Introduction</strong></div><ul><li>PC Overview</li><li>Course Introduction</li><li>Course Overview</li><li>Reading: Helpful Tips for Course Completion</li></ul><div> </div><div><strong>Module 1: Introduction to Digital Marketing</strong><br /><br /></div><div>Basics of Digital Marketing</div><ul><li>Module Summary and Learning Objectives</li><li>Introduction to Digital Marketing</li><li>Reading: Digital Marketing – Growth and Opportunities</li><li>Podcast: Digital Marketing – Growth and Opportunities</li><li>Reading: Introduction to Growth Hacking</li><li>Podcast: Introduction to Growth Hacking</li><li>Digital Marketing versus Traditional Marketing</li><li>Key Digital Marketing Channels</li><li>Activity: Channel Selection Challenge</li><li>Introduction to Digital Marketing Analytics</li><li>Reading: Key Terms Used in Digital Analytics</li><li>Practice Quiz: Basics of Digital Marketing</li></ul><div>Digital Strategy and Business Alignment</div><ul><li>Importance of Digital Strategy</li><li>Components of a Digital Strategy</li><li>Reading: Aligning Marketing with Business Goals</li><li>Reading: Aligning Growth Hacking with Business Goals</li><li>Lab: Exploring Key Components of a Digital Strategy</li><li>Practice Quiz: Digital Strategy and Business Alignment</li></ul><div>Module Summary</div><ul><li>Module Summary: Introduction to Digital Marketing</li><li>Podcast Recap: Introduction to Digital Marketing</li><li>Graded Quiz: Introduction to Digital Marketing</li></ul><div> </div><div><strong>Module 2: Digital Marketing Channels and Audiences<br /></strong><br /></div><div>Digital Channels in Marketing</div><ul><li>Module Summary and Learning Objectives </li><li>Introduction to Digital Channels</li><li>Introduction to Organic Digital Channels</li><li>Paid Media Channels</li><li>Email Marketing and Automation</li><li>Reading: Aligning Channels with Digital Objectives</li><li>Reading: Aligning Growth Hacking with Your Digital Channel Strategy</li><li>Podcast: Aligning Growth Hacking with Your Digital Channel Strategy</li><li>Lab: Develop a Channel Strategy</li><li>Practice Quiz: Digital Channels in Marketing</li></ul><div>Digital Target Audiences</div><ul><li>Introduction to Target Audience Analysis</li><li>Role of Demographics in Audience Analysis</li><li>Role of Psychographics in Audience Analysis</li><li>Reading: Using Audience Data for Targeting</li><li>Reading: Identifying Growth Hacking Opportunities in Audience Targeting</li><li>Lab: Create Digital Personas</li><li>Practice Quiz: Digital Target Audiences</li></ul><div>Module Summary</div><ul><li>Module Summary: Digital Marketing Channels and Audiences</li><li>Podcast Recap: Digital Marketing Channels and Audiences</li><li>Graded Quiz: Digital Marketing Channels and Audiences</li></ul><div> </div><div><strong>Module 3: Digital Metrics and Career Prospects</strong></div><div> </div><div>Digital Marketing Analytics and Metrics</div><ul><li>Module Summary and Learning Objectives </li><li>Introduction to Marketing Metrics and Analytics</li><li>Key Performance Indicators (KPIs): Overview</li><li>Understanding Traffic and Conversions</li><li>Reading: ROI and Engagement Metrics</li><li>Reading: Tools for Tracking Marketing Performance</li><li>Reading: Analyzing Marketing Data for Actionable Insights</li><li>Reading: Analyzing Data for Growth Hacking</li><li>Lab: Analyze Performance Metrics</li><li>Practice Quiz: Digital Marketing Analytics and Metrics</li></ul><div>Digital Marketing Career Prospects</div><ul><li>Job Roles in Digital Marketing</li><li>Reading: Growth Hacking in Digital Marketing</li><li>Podcast: Growth Hacking in Digital Marketing</li><li>Digital Marketing Industry Growth</li><li>Career Paths and Growth in Digital Marketing</li><li>Reading: Digital Marketing Opportunities for Small Business Owners and Freelancers</li><li>Reading: Future Trends in Digital Marketing</li><li>Podcast: Future Trends in Digital Marketing</li><li>Reading: Career Growth Opportunities as a Growth Hacker</li><li>Podcast: Career Opportunities as a Growth Hacker</li><li>Practice Quiz: Digital Marketing Career Prospects</li></ul><div>Module Summary</div><ul><li>Module Summary: Digital Metrics and Career Prospects</li><li>Graded Quiz: Digital Metrics and Career Prospects</li></ul><div><br /><strong>Module 4: Final Project and Assessment</strong></div><ul><li>Module Summary and Learning Objectives</li></ul><div>Final Project</div><ul><li>Project Overview: Develop a Digital Marketing Strategy</li><li>Final Submission: Develop a Digital Marketing Strategy </li></ul><div>Glossary and Final Exam</div><ul><li>Glossary: Essential Concepts in Digital Marketing</li><li>Final Exam: Introduction to Digital Marketing</li></ul><div>Wrap-Up</div><ul><li>Course Wrap-Up</li><li>Congratulations and Next Steps</li><li>Thanks from the Course Team</li></ul><div><br /></div>
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Digital Marketing: Fundamentals

Get started with digital marketing and growth hacking. Learn the fundamentals of digital strategy, digital channels, audience targeting & insights, and performance metrics, and what it takes to become a digital marketing professional.

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  <div><br />This course comprises seven modules. <br /><br /></div><div>We encourage you to set aside approximately four hours each week to successfully complete this course in four weeks.<br /><br /></div><div>To derive maximum benefit from this course, watch all the videos and review the readings. Then, reinforce your learning by completing all the hands-on activities, such as the labs. Take the practice and graded assessment to test your knowledge. Finally, apply your skills to the final project and complete the final assessment to consolidate your learning.<br /><br /></div><div><strong>Module 1: Campaign Strategy and Audience Targeting <br /></strong><br /></div><div>In this module, you will begin your capstone project by selecting a hypothetical product or service, defining its business objectives, and setting SMART marketing goals. Through hands-on labs, you will develop buyer personas and align them with these objectives, while identifying the most effective digital channels to reach your audience. By the end, you will have a strategic campaign brief to guide the rest of your project.<br /><br /></div><div><strong>Module 2: SEO and Content Marketing Plan <br /><br /></strong>In this module, you will design an SEO-driven content marketing plan that directly supports your overall campaign strategy. Through hands-on labs, you will conduct targeted keyword research aligned with the hypothetical brand’s goals, develop optimized page titles and meta descriptions, and outline a blog post to drive organic traffic. You will also create a seven-day content calendar to plan and structure high-impact posts across relevant platforms, ensuring consistency and audience alignment. Emphasizing strategic thinking, measurable outcomes, and SEO best practices, this module equips you to enhance visibility, engagement, and long-term content value. By the end, you will have a complete, ready-to-deploy content plan.<br /><br /></div><div><strong>Module 3: Digital Advertising<br /></strong>In this module, you’ll bring your capstone project to life with a hands-on dive into paid advertising. Through interactive labs, you will design a targeted ad campaign that perfectly aligns with your overall strategy. You’ll craft attention-grabbing ad copy for platforms such as Google and Meta, develop a precise ad targeting plan to reach your ideal audience segments, and map out creative concepts for high-impact visuals. You will also estimate budgets, choose winning bidding strategies, and forecast key performance indicators (KPIs) to measure success. By the end, you’ll have a polished, ready-to-launch paid media plan that’s built to deliver results.<br /><br /></div><div><strong>Module 4: Social Media Content Plan<br /></strong>In this module, you will master the art of creating an organic social media strategy that works. You will design a strategic seven-day content plan for your hypothetical brand, choosing the right platforms, aligning posts with campaign goals, and applying growth hacking techniques to spark engagement. You will explore visual storytelling, smart hashtag strategies, and the craft of writing captions that stop the scroll. In hands-on labs, you will bring your ideas to life by designing and refining posts with tools such as Canva while experimenting with platform-specific tactics to amplify reach. By the end, you will have a fully developed social media calendar packed with tailored content built to captivate your audience and boost brand visibility.<br /><br /></div><div><strong><br />Module 5: E-commerce and Email Marketing <br /></strong>In this module, you will learn to turn clicks into customers through powerful product pages and high-impact email marketing. You will master the principles of conversion-focused design and personalized messaging to boost engagement and sales. Step by step, you will create a visually striking, search-optimized product page with persuasive copy, attention-grabbing visuals, and compelling pricing. You will also design an email marketing sequence that speaks directly to segmented audiences, building trust and driving action. In hands-on labs, you will craft a mock product listing and develop a two-part email series that nurtures relationships and inspires purchases. By the end, you will have the skills to generate traffic, captivate customers, and grow sales through integrated e-commerce and email strategies.<br /><br /></div><div><strong>Module 6: Data Analysis for Growth<br /></strong>In this module, you will unlock the power of data to fuel growth and maximize return on investment (ROI). You will explore how smarter analysis and sharper insights can transform performance, diving into real-world data from SEO, social media, and digital advertising. Through hands-on activities, you will work with data across multiple digital channels, identify opportunities, and apply optimization techniques that deliver measurable results. By the end, you will have the skills to turn raw numbers into strategic actions that drive sustained growth.<br /><br /></div><div><strong><br />Module 7: Final Course Project and Course Wrap-Up<br /></strong>In this final module, you will compile your project work into a professional campaign portfolio and create a compelling presentation that showcases your analytical and communication skills. You will learn to structure and design presentations for maximum clarity, engagement, and impact, applying best practices used by industry professionals. The portfolio and presentation you produce can be leveraged for job applications, interviews, or freelance opportunities, giving you a tangible asset to demonstrate your expertise. You will submit your final presentation highlighting the insights and results from your project, while the module concludes with a review of key takeaways and next steps for applying your new skills.<br /><br /></div><div><br />Additionally, you’ll gain access to bonus career-focused content, including tips on job searching, resume building, interview preparation, freelancing success, and creating a digital portfolio.<br /><br /></div>
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Capstone Project: Digital Marketing and Growth Hacking

Throughout the project, you’ll work with platforms like Facebook and LinkedIn and tools such as Shopify, Canva, and Zoho Campaigns. In parallel, you’ll use generative AI tools like ChatGPT and Gemini to enhance assets and improve efficiency.To wrap up, you’ll design a final presentation that demonstrates your analytical and presentation skills. The course also provides career guidance, from building your digital portfolio to resume, LinkedIn, and interview tips, plus insights on freelancing success.

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  <div>After completing this course, you should be able to:</div><ul><li>Develop projects more efficiently</li><li>Understand the fundamental skills of an project</li><li>Understand when and how to use a “Decision”, “Workflow”, and “Generative AI”</li><li>Turn a conceptual idea into a functional project on Orchestrate</li><li>Understand and apply different versions of an project and successfully deploy it</li></ul>
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IBM watsonx Orchestrate: Start with Projects in Skill Studio

In this course, you learn how to create an project on IBM watsonx Orchestrate to streamline a Human Resources (HR) process that involves defining a decision on when the salary range could be automatically approved or sent for manual review.

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  <div>In this Capstone Project, you will:</div><ul><li>Develop scalable backend applications using JavaScript, Node.js, Express, and MongoDB, with full CRUD functionality for database interaction.</li><li>Design and implement secure RESTful APIs using Node.js, including user authentication and authorization with JWT.</li><li>Integrate a MongoDB service into backend applications to support data persistence and dynamic application behavior.</li><li>Containerize backend applications using Docker and deploy them using serverless technologies such as Kubernetes and IBM Code Engine.</li><li>Apply CI/CD practices and Agile workflows by building pipelines and managing development tasks with Kanban boards.</li><li>Evaluate and test microservices for functionality, performance, and reliability to ensure a robust and maintainable architecture.</li></ul><div><strong>The following skills are required to be successful with this course: </strong></div><div> </div><ul><li>We recommend completing all previous courses in the IBM Backend Developer – JavaScript – Professional Certificate before starting this capstone.</li><li>Through these courses, you will have developed working knowledge of the following required for this course: <ul><li>Software Engineering</li><li>HTML &amp; CSS</li><li>Version control with Git </li><li>JavaScript</li><li>Node.js &amp; Express</li><li>Cloud Native, DevOps, and Agile</li><li>NoSQL and MongoDB</li><li>Containers with Docker and Kubernetes</li><li>Microservices &amp; Serverless</li></ul></li></ul><div>Demonstrate your readiness for real-world development roles by building a professional-grade backend application from the ground up. In this capstone project, you’ll take on a practical, industry-inspired challenge that puts your JavaScript backend skills into action.<br /><br /></div><div>This course offers the opportunity to solidify your technical capabilities by designing and implementing a scalable microservices architecture using JavaScript, Node.js, Express, and MongoDB. You’ll also apply modern development practices such as CI/CD and Agile project management.<br /><br /></div><div>Throughout the capstone, you will apply in-demand skills, including:</div><ul><li>Designing and building RESTful APIs with robust error handling</li><li>Developing MongoDB-based APIs to perform full CRUD operations</li><li>Testing and invoking microservices to ensure reliability</li><li>Securing the backend using JWT authentication best practices</li><li>Integrating your backend with a frontend and deploying a full-stack app using IBM Code Engine and Kubernetes</li></ul><div>By the end of the course, you’ll have a fully functional backend application to showcase in your portfolio and to potential employers.<br /><br /></div><h1>Course Syllabus</h1><div><strong>Module 1: Project Introduction, Repository Setup, User Stories and Database Setup</strong></div><ul><li>Lesson 0: Course Introduction</li><li>Lesson 1: Develop User Stories</li><li>Lesson 2: Set Up MongoDB and Populate Data</li><li>Lesson 3: Module Summary and Assessment </li></ul><div><strong>Module 2 : Back-end APIs and Services</strong></div><ul><li>Lesson 1 : Implement Listings</li><li>Lesson 2 : Implement the Search API</li><li>Lesson 3 : Implement Sentiment Analysis Service</li><li>Lesson 4 : Module Summary and Assessment </li></ul><div><strong>Module 3 : User Management with JWT</strong></div><ul><li>Lesson 1 : Implement Registration Back End</li><li>Lesson 2 : Implement Login Back End</li><li>Lesson 3: Implement User Profile Back End</li><li>Lesson 4: Integrate Back End to the Front End</li><li>Lesson 5 : Module Summary and Assessment </li></ul><div><strong>Module 4 : CI/CD, Containerize, and Deploy</strong></div><ul><li>Lesson 1 : CI/CD with GitHub Actions</li><li>Lesson 2 : Containerize and Deploy the Application</li><li>Lesson 3: Module Summary and Assessment </li></ul><div><strong>Module 5 : Final Project Submission and Peer Review</strong></div><ul><li>Lesson 1 : Final Project Submission and Peer Review</li><li>Lesson 2 : Course Wrap Up</li></ul>
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Capstone Project: JavaScript Backend Development

Demonstrate your backend development skills with JavaScript, Node.js, Express, and MongoDB. Build secure APIs, use JWT, and deploy with Docker, Kubernetes, and Serverless.

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