<div>Prepare for a career as a Deep Learning Engineer. Gain the in-demand skills and hands-on experience to get job-ready in less than 3 months.<br><br>In this professional certificate, you will:&nbsp;<br><br></div><ul><li>Define the key concepts of deep learning, including supervised learning, unsupervised learning, reinforcement learning, and neural network architectures such as CNNs and transformers.</li><li>Describe the applications and workings of popular deep learning libraries like PyTorch, Keras, and TensorFlow.</li><li>Apply deep learning frameworks and models to real-world problems, including image recognition, natural language processing, and time series prediction.</li><li>Analyze and evaluate the performance of different deep learning models, comparing models trained with various libraries.</li><li>Create custom models and layers using Keras, and integrate Keras with TensorFlow 2.X for advanced tasks.</li><li>Evaluate the results of deep learning projects, focusing on the interpretation of model performance and the optimization of neural network architectures.</li><li>Combine knowledge from multiple domains (machine learning, deep learning, neural networks) to construct end-to-end solutions and deploy them effectively.</li></ul><div>&nbsp;<br><strong>Program Overview</strong>&nbsp;<br><br></div><div>The global deep learning market is set to grow 23% annually to 2030 (Grand View Research). The IBM Deep Learning Professional Certificate builds the job-ready skills and practical experience needed to thrive in the rapidly advancing AI field.</div><div>&nbsp;</div><div>Deep learning, a core subset of machine learning, drives the generative AI revolution. This certificate program will equip you with the skills to build, train, and deploy deep learning models. Through hands-on projects, you’ll learn how to use deep neural networks to simulate human-like decision-making abilities, gaining a strong foundation in Python and key machine learning frameworks.</div><div>&nbsp;</div><div>You’ll learn to develop models using PyTorch, Keras, and TensorFlow, applying them to real-world challenges in areas like image recognition, natural language processing (NLP), and time series prediction. The program covers supervised learning, reinforcement learning, transformer models, and unsupervised learning methods.</div><div>&nbsp;</div><div>Along the way, you’ll build custom layers and models, create advanced convolutional neural networks (CNNs), and integrate Keras with TensorFlow 2.x. You’ll gain hands-on experience through practical labs and projects, where you’ll tackle real-world AI problems and create robust models that employers demand.</div><div>&nbsp;</div><div>By the end of the program, you will be well-equipped with industry-standard tools and techniques, having completed multiple projects, including a comprehensive capstone project, to showcase your deep learning expertise. Whether you’re looking to advance your current AI or data science career or start fresh, this Professional Certificate will provide you with the skills, knowledge, and practical experience to succeed in the growing deep learning job market.</div><div>&nbsp;</div><div>This program is for anyone interested in mastering Deep Learning.&nbsp; This program is ideal for professionals already in related roles, such as data scientists, software engineers, machine learning engineers, data engineers, and Python developers, who want to transition to a rewarding AI engineering career.</div><div><br><strong>Prerequisites<br><br></strong>The following skills are required to be successful in this program:&nbsp;<br><br></div><ul><li>High School level Mathematics</li><li>Working knowledge of Python</li><li>Machine Learning with Python</li></ul>

IBM Deep Learning Professional Certificate

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  <div>CAPM certification helps individuals with up to three years of experience in project management boost their resumes and make their job applications stand out. (Forbes). <br /><br /></div><div>This course helps talented project professionals to comprehensively prepare for this career-enhancing CAPM exam.  <br /><br /></div><div>During the course, you’ll get an overview of CAPM exam requirements, including education, experience, and associated costs. This includes: <br /><br /></div><div><strong>The CAPM exam registration process</strong>  <br /> Understand the steps required to register for the CAPM certification exam, with insights into eligibility requirements.  <br /><br /></div><div><strong>CAPM exam details</strong>  <br /> Learn about the structure of the CAPM exam, including the types of questions, exam format, and what to expect on test day.  <br /><br /></div><div><strong>CAPM exam domains</strong>  <br /> The CAPM exam is organized into four domains. You will explore the scope of each domain scope and understand their weighting in the exam.  <br /><br /></div><div><strong>Study resources and tips</strong>  <br /> Access resources and tips on preparing for the CAPM exam. This practical advice will help you create an effective study plan and utilize the provided practice tests to assess your readiness.  <br /><br /></div><div><strong>150-question mock exam</strong>  <br /> Complete a comprehensive 150-question mock exam which simulates the real CAPM exam experience, with a 3-hour time frame that mirrors the actual test.  <br /><br /></div><div>If you’re ready to power up your resume with a CAPM certification that catches the attention of employers, ENROLL TODAY. You’ll be exam ready in just 2 weeks! <br /><br /></div><div> </div><div><strong>You will learn:</strong></div><ul><li>Comprehensively prepare for the industry-recognized CAPM certification exam in just 2 weeks!  </li><li>Learn effective strategies and techniques to optimize your exam results, such as time management, question prioritization, and leveraging strengths </li><li>Understand the CAPM exam format, duration, topics, and objectives, enabling you to focus on the topics that matter most </li><li> Complete a 150-question mock exam that simulates the real examination environment. </li></ul><div><br /></div><h1>Course Syllabus</h1><div><br /><strong>Welcome</strong></div><ul><li>Video: Course Introduction </li><li>Reading: General Information </li><li>Reading: Learning Objectives and Syllabus </li><li>Reading: Grading Scheme </li><li>Reading: How to Make the Most of this Course</li></ul><div><strong>Module 1: CAPM Certification Test Overview</strong></div><ul><li>Reading: Module Introduction and Learning objectives  </li><li>Reading: CAPM Certification Exam Eligibility  </li><li>Video: Information on the CAPM Exam  </li><li>Reading: Overview of CAPM Exam Domains  </li><li>Reading: Breakdown of CAPM Exam Domains  </li><li>Video: Exam Taking Strategies  </li><li>Reading: Additional Study Resources and Tips  </li><li>Reading: Summary: Module 1: CAPM Certification Test Overview  </li><li>Reading: Check your readiness  </li><li>Practice Quiz: Domain 1 Project Management Fundamentals and Core Concepts  </li><li>Practice Quiz: Domain 2 Predictive Plan Based Methodologies  </li><li>Practice Quiz: Domain 3 Agile Framework and Methodologies  </li><li>Practice Quiz: Domain 4 Business Analysis Frameworks </li></ul><div><strong>Module 2: CAPM Mock Test and Next Steps </strong></div><ul><li>Reading: Module Introduction and Learning objectives  </li><li>Reading: Exam Instructions  </li><li>Graded Quiz: Graded Practice Exam for CAPM Certification  </li><li>Retake Exam  </li></ul><div><strong>Module: Course Wrap-Up</strong></div><ul><li>Reading: Thanks from the Course Team  </li><li>Reading: Congratulations and Next Steps  </li><li>Reading: Copyrights and Trademarks  </li></ul><div><br /><strong>Course Rating &amp; Feedback </strong></div><ul><li>Feedback  </li></ul><div><br /></div><div><br /><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>Learners should have completed prior courses in their associated program. However, an interest in practicing the CAPM exam is all that is required. </div>
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Project Management Practice Exam: CAPM Certification

Prepare for the CAPM exam with comprehensive insights, study resources, and a mock exam, and get ready to power up your resume with this industry-recognized certification. 

<div>Prepare for a career as a Generative AI Engineer. Gain the in-demand skills and hands-on experience to get job-ready in less than 6 months!<br><br>In this professional certificate, you will:&nbsp;<br><br></div><ul><li>Describe the core principles of Generative AI, machine learning, deep learning, NLP applications, and large language models by explaining their key components, functionalities, and how they are applied in real-world scenarios.</li><li>Apply Python libraries such as Flask, SciPy, Scikit-learn, Keras, and PyTorch to design, build, and deploy GenAI applications, AI-powered chatbots, and intelligent agents that interact with users dynamically.</li><li>Analyze different Generative AI architectures and NLP models, including transformers like BERT and large language models like GPT, by evaluating their structure, strengths, and limitations for various AI-driven tasks.</li><li>Evaluate the effectiveness of advanced AI techniques such as prompt engineering, model training, and fine-tuning by comparing different strategies and determining their impact on model accuracy, efficiency, and overall performance.&nbsp;</li><li>Create and deploy a fully functional Generative AI application by integrating machine learning techniques, LLMs, and frameworks such as RAG and LangChain to solve real-world NLP challenges.&nbsp;</li><li>Assess the ethical implications, risks, and potential biases associated with GenAI technologies by critically examining their societal impact and proposing responsible AI development and deployment strategies.</li></ul><div><br><strong>Program Overview</strong><br><br>The Generative AI industry is projected to expand at a CAGR of over 46% through 2030 (Statista). As a result, the demand for tech professionals skilled in Generative AI engineering is skyrocketing!</div><div>&nbsp;</div><div>The IBM Generative AI Engineering Professional Certificate equips aspiring Generative AI engineers, AI developers, data scientists, machine learning engineers, and AI research engineers with the critical expertise in Generative AI, large language models (LLMs), and natural language processing (NLP) needed to capture employers’ attention.</div><div>&nbsp;</div><div>A GenAI engineer designs AI systems that generate new data—such as text, images, audio, and video—by leveraging transformers and LLMs. This program immerses you in AI, Generative AI, and prompt engineering, alongside data analysis, machine learning, and deep learning with Python. You’ll work with libraries like SciPy and scikit-learn while developing applications using frameworks and models such as BERT, GPT, and LLaMA. You’ll also utilize Hugging Face Transformers, PyTorch, RAG, and LangChain to build and deploy LLM-based NLP applications, gaining expertise in tokenization, language models, and transformer methodologies.</div><div>&nbsp;</div><div>Through hands-on labs and projects, you’ll gain practical experience that will strengthen your resume and help you stand out in interviews. Plus, you’ll complete a major guided project where you’ll develop your own real-world GenAI application.</div><div>&nbsp;</div><div>If you’re ready to distinguish yourself with highly sought-after Generative AI skills, ENROLL TODAY and take your career to the next level in under six months!</div><div>&nbsp;</div><div><strong>Applied Learning Project</strong></div><div>&nbsp;</div><div><strong>Practical Experience That Employers Value</strong></div><div>&nbsp;</div><div>Employers prioritize hands-on experience when evaluating candidates. This Professional Certificate ensures you gain the practical expertise to demonstrate your capabilities in a job interview.</div><div>&nbsp;</div><div>Your hands-on learning includes:<br><br></div><ul><li>Generating text, images, and code using GenAI</li><li>Applying prompt engineering techniques and best practices</li><li>Developing multiple GenAI applications with Python and deploying them using Flask</li><li>Building an NLP data loader</li><li>Training a simple language model with a neural network</li><li>Using transformers for classification and creating a translation model</li><li>Implementing prompt engineering and in-context learning</li><li>Fine-tuning models to enhance performance</li><li>Leveraging LangChain tools and components for various applications</li><li>Constructing AI agents and applications with RAG and LangChain in a comprehensive guided project.</li></ul><div><br><strong>Prerequisites<br><br></strong>The following skills are required to be successful in this program:&nbsp;</div><div>&nbsp;</div><ul><li>Basic computer literacy</li><li>Working knowledge of Python</li></ul>

IBM Generative AI Engineering PC

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  <div>Explore the revolutionary capabilities of DALL·E in AI-driven image generation, transforming simple text prompts into stunning visuals. This guided project empowers developers, educators, and creatives to harness the power of OpenAI's DALL·E models, enabling them to create unique images in just 30 minutes. Learn how to craft effective prompts, utilize the OpenAI API, and experiment with various parameters to enhance your creative projects. Perfect for anyone looking to innovate in digital art, design, or content creation!<br /><br />DALL·E, an advanced AI model developed by OpenAI, is revolutionizing the way we create and visualize content. By bridging the gap between textual inputs and visual outputs, it offers an unprecedented tool for creativity and innovation. Whether you're a developer looking to integrate cutting-edge technology, an educator aiming to bring new dimensions to your teaching, or a creative professional seeking to expand your artistic toolkit, this project is your gateway to mastering AI-driven image generation.<br /><br /><strong>What You'll Learn</strong><br />After completing this project, you will be able to:<br />- Understand the fundamentals of DALL·E and its role in AI-driven image generation.<br />- Craft effective prompts to guide DALL·E in producing desired visual outputs.<br />- Utilize the OpenAI API to create and manage image generation projects.<br />- Experiment with various prompts to customize and enhance your creative projects.<br /><br /><strong>What You'll Need</strong><br />To get the most out of this guided project, you should have:<br />- Basic familiarity with programming concepts.<br />- A working understanding of APIs and their integration.<br />- Access to a computer with internet connectivity and a modern web browser like Chrome, Edge, Firefox, Internet Explorer, or Safari.<br /><br />Dive into this transformative project and unlock the potential of AI to elevate your digital creations to new heights!<br /><br /></div>
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DALL·E: Image Generation Guide for Beginners

Learn to use OpenAI's API to interact with DALL·E 2 and DALL·E 3, turning simple text ideas into breathtaking visuals. This hands-on project is designed to help developers and educators tap into the potential of DALL·E models, making it possible to create one-of-a-kind images in just 30 minutes. You'll get tips on writing effective prompts, using the OpenAI API, and tweaking settings to bring your creative ideas to life. Whether you're into digital art, design, or content creation, this is a great way to push your creative boundaries.


<div>Kickstart your career as a cybersecurity analyst. Gain the essential skills needed for this highly in-demand role—no degree or prior experience necessary.<br><br>In this professional certificate, you will:&nbsp;<br><br></div><ul><li>Gain the essential skills to secure an entry-level cybersecurity analyst role in just 4–6 months.</li><li>Develop a strong foundation in cybersecurity, including managing database vulnerabilities, operating system administration, and security protocols.</li><li>Learn to apply cybersecurity tools and techniques for penetration testing, incident response, and digital forensics.</li><li>Enhance your productivity and effectiveness as a cybersecurity analyst by leveraging generative AI.</li><li>Prepare for highly valued CompTIA Security+ and CySA+ Certifications.</li></ul><div><br><strong>Program Overview<br><br></strong>This IBM Professional Certificate equips you with the essential skills to help you land your first role as a cybersecurity analyst—no prior experience or degree required.<br><br></div><div>Cybersecurity analysts safeguard an organization’s systems and networks by detecting security breaches, analyzing threats, and implementing protective measures to prevent cyberattacks.<br><br></div><div>Through this program, you’ll gain a strong foundation in cybersecurity principles, tools, and technologies. You’ll explore topics such as cyber threats, operating systems, networking fundamentals, database security, vulnerabilities, cybersecurity architecture, and compliance frameworks. Additionally, you’ll learn how to leverage generative AI for cybersecurity and develop essential job search strategies, including resume building and interview preparation.<br><br></div><div>You’ll also gain hands-on experience in penetration testing, incident response, forensics, and threat intelligence. Real-world projects will allow you to create cybersecurity plans and compliance frameworks, giving you valuable practical knowledge to showcase in job interviews.<br><br></div><div>Upon successful completion, you’ll receive an IBM Professional Certificate and a digital badge to enhance your resume and demonstrate your expertise to employers.<br><br></div><div>This Professional Certificate is designed to provide practical experience that aligns with industry expectations. Throughout the program, you’ll engage in interactive labs and projects to refine your skills, including investigating real-world cybersecurity cases to build expertise in incident response and forensics, gaining hands-on practice with industry-standard security tools. You’ll be applying cybersecurity best practices to mitigate risks, enhance security, and ensure compliance, using generative AI to improve cybersecurity strategies and efficiency, and analyzing a security breach by identifying attack vectors, vulnerabilities, financial impacts, and prevention strategies.<br><br></div><div>Take the next step toward a rewarding career in cybersecurity—enroll now!</div><div><strong><br>Prerequisites&nbsp;</strong></div><div><br>No degree or prior experience in Cybersecurity required. You should have basic computer literacy.&nbsp;</div><div><br><br></div>

IBM Cybersecurity Analyst Professional Certificate

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  <div><br />Generative AI skills are in demand, with Ascend.io reporting that 89% of respondents reporting using generative AI in some capacity. ZipRecruiter reports that data engineers with generative AI skills earn an average of USD 115,000 annually, with top-end salaries of USD 179,000 annually.<br /><br /></div><div>Data engineering is responsible for building strong data pipelines, managing data infrastructure, and ensuring high-quality data evaluation.<br /><br /></div><div>This course is suitable for existing and aspiring data engineers, data warehousing specialists, and other data professionals such as data analysts, data scientists, and BI analysts.<br /><br /></div><div>First, learn about the current impact of generative AI on data engineering.<br /><br /></div><div>Throughout the course, you will assume the role of a data engineer and gain experience using generative AI to enhance productivity by introducing innovative ways to deliver projects.<br /><br /></div><div>You will learn how to use and apply generative models for tasks such as architecture design, database querying, data warehouse schema design, data augmentation, data pipelines, ETL workflows, data analysis and mining, data lake house, and data repositories. You will also explore challenges and ethical considerations associated with using Generative AI.<br /><br /></div><div>To complete this course, you'll demonstrate your new generative AI skills in a real-world, shareable, hands-on data engineering project.<br /><br /></div><div>After you successfully complete your final quiz, you will receive your certificate, and you can request your Badge. You can share both your project and certificate with your current or prospective employers.</div><h1>Course Syllabus</h1><div><strong>Module 1: Data Engineering and Generative AI<br /></strong><br /></div><ul><li>Welcome</li><li>Data Engineering and Generative AI</li><li>Generative AI for Data, Database, Warehouse, and Design</li><li>Module Summary and Quiz</li></ul><div><strong>Module 2: Use of Generative AI for Data Engineering<br /></strong><br /></div><ul><li>Generative AI for ETL and Data Repositories</li><li>Generative AI Considerations in Data Engineering</li><li>Module Summary and Quiz</li></ul><div><strong>Module 3: Guided Project and Final Exam<br /></strong><br /></div><ul><li>Guided Project</li><li>Final Exam</li><li>Course Wrap-Up</li></ul><div><br /></div><h1>General Information</h1><div><strong><br />What you'll learn<br /></strong><br /></div><ul><li>Use generative AI tools and techniques in data engineering processes across industries</li><li>Apply generative AI solutions for data generation, augmentation, and anonymization</li><li>Evaluate real-world case studies that feature successful application of Generative AI for ETL and data repositories</li><li>Build generative AI skills in hands-on labs and projects for data warehouse schema design and infrastructure setup</li></ul>
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Mastering Generative AI for Data Engineering

Learn to apply AI models to enhance productivity for tasks like architecture design, database querying, ETL workflows, and data analysis. Explore ethical considerations and prove your skills with a real-world project. Unlock generative AI skills for data engineering. Boost your skills to revolutionize data pipelines, boost productivity, and stay ahead in the rapidly evolving tech landscape. Complete a shareable project and earn an industry certificate

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  <div>Do you want to enhance your software development career by leveraging the power of generative AI? This course explores the transformative applications of generative AI across the entire software development lifecycle. <br /><br />The course teaches how generative AI-based tools enable code generation, scripting, and program creation, boosting developer productivity and automating repetitive tasks. You will learn how to train and fine-tune these models for specific software development needs, from generating custom code snippets to creating intelligent applications. <br /><br />You will gain hands-on experience building static websites and prototypes using AI, accelerating the design process and fostering innovation. The course covers techniques for AI-assisted design and architecture, allowing you to explore new creative possibilities and optimize software structure. <br /><br />You will also discover how generative AI can be used for bug detection, code refactoring, and program optimization, leading to improved code quality and maintainability. The course provides insights into the ethical considerations, challenges, and limitations of using generative AI in software development, emphasizing responsible AI practices. <br /><br />Build a standout portfolio with hands-on labs and real-world projects, applying generative AI to real software development scenarios. From AI-powered features to streamlined workflows, you'll gain the practical experience employers demand. Plus, you'll learn to evaluate the impact of generative AI, preparing you for the future of software engineering. Enroll now and future-proof your career!  <br /><br />Job-ready AI-powered software development skills in less than 1 month, plus you’ll get practical experience How to generate code snippets, scripts, and programs using generative AI models, automating development tasks Hands-on experience training and fine-tuning generative AI models for software development needs, creating custom solutions How to create static websites and prototypes using AI, accelerating design and development processes.<br /><br /></div><h1>Learning Objectives</h1><ul><li>Job-ready AI-powered software development skills in less than 1 month, plus you’ll get practical experience  </li><li>How to generate code snippets, scripts, and programs using generative AI models, automating development tasks  </li><li>Hands-on experience training and fine-tuning generative AI models for software development needs, creating custom solutions  </li><li>How to create static websites and prototypes using AI, accelerating design and development processes  </li></ul><h1>Course Syllabus</h1><div><strong>Module 0: Welcome </strong> </div><ul><li>Video: Course Introduction  </li><li>Reading: General Information  </li><li>Reading: Learning Objectives and Syllabus  </li><li>Reading: Grading Scheme  </li></ul><div><strong>Module 1: Generative AI and Software Development </strong> </div><ul><li>Reading: Module Introduction and Learning Objectives  </li><li>Video: Introduction to Generative AI in Software Development  </li><li>Video: Meet Your Experts  </li><li>Video: Gen AI in Software Development  </li><li>Video: Leveraging Generative AI in Software Development Lifecycle  </li><li>Video: Large Language Models (LLM) and Transformers  </li><li>Video: Natural Language Processing (NLP) and Its Significance  </li><li>Reading: Popular Tools of Generative AI for Software Development  </li><li>Lab: OpenAI Account Setup and API Key Generation  </li><li>Optional Lab: Setting Up the Development Environment Using Visual Code Studio  </li><li>Lab: Building a Basic Chatbot using ChatGPT  </li><li>Lab: Get Familiar with Generative AI Classroom  </li><li>Video: Leveraging AI for Technical Help and Best Practices on Design Patterns and Architecture  </li><li>Video: AI for Coding and Development  </li><li>Lab: Generate Database Design with ChatGPT  </li><li>Lab: Get a Solution to the Given Coding Problem  </li><li>Demo Video: GitHub CoPilot for Code Generation and Troubleshooting  </li><li>Generating Static Website using Generative AI  </li><li>Demo Video: Generating Prototypes with Generative AI  </li><li>Reading: Generating Architecture and Design Diagrams Using Generative AI  </li><li>Demo Video: Generating Architecture and Design Diagrams Using Generative AI  </li><li>Reading: Managing Legacy Code with Gen AI  </li><li>Video: Gen AI for Technical Help and Software Architecture  </li><li>Module 1 Practice Quiz: Generative AI and Software Development  </li><li>Module 1 Summary: Generative AI and Software Development  </li><li>Reading: Useful Prompts for Software Design and Development  </li><li>Module 1 Graded Quiz: Generative AI and Software Development  </li><li>Optional Reading Setting Up Your Own AI Development Environment with ChatGPT  </li></ul><div><strong>Module 2: Generative AI for Software Development Workflows and its Considerations </strong> </div><ul><li>Reading: Module Introduction and Learning Objectives  </li><li>Video: Introduction to CI/CD Using AI  </li><li>Video: Gen AI for CI/CD Tools  </li><li>Lab: Write a Dockerfile for a Node App Deployment  </li><li>Video: Generative AI for Security in Software  </li><li>Video: Popular Generative AI Tools for Security in Software  </li><li>Video: Generative AI for Software Testing  </li><li>Lab: Generate Test Cases for Specific Use Case Using Generative AI  </li><li>Video: Integration of Generative AI Into Software Development Workflows  </li><li>Lab: Software Documentation Using Chat GPT  </li><li>Lab: Review Code with Generative AI  </li><li>Reading: Module Introduction and Learning Objectives  </li><li>Video: Introduction to CI/CD Using AI  </li><li>Video: Gen AI for CI/CD Tools  </li><li>Lab: Write a Dockerfile for a Node App Deployment  </li><li>Video: Generative AI for Security in Software  </li><li>Video: Popular Generative AI Tools for Security in Software  </li><li>Video: Generative AI for Software Testing  </li><li>Lab: Generate Test Cases for Specific Use Case Using Generative AI  </li><li>Video: Integration of Generative AI Into Software Development Workflows  </li><li>Lab: Software Documentation Using Chat GPT  </li><li>Lab: Code Translation Using Generative AI  </li><li>Lab: Review Code with Generative AI  </li><li>Reading: Considerations for Software Development for Generative AI  </li><li>Reading: Ethical Considerations for Using Generative AI in Software Development  </li><li>Video: Ethical Considerations for Software Development in AI  </li><li>Video: Innovation with Generative AI in Software Development  </li><li>Module 2 Practice Quiz: Generative AI for Software Development Workflows and Its Considerations  </li><li>Module 2 Cheatsheet: Generative AI for Software Development Workflows and its Considerations  </li><li>Module 2 Summary: Generative AI for Software Development Workflows and its Considerations  </li><li>Module 2: Generative AI for Software Development Workflows and its Considerations  </li></ul><div><strong>Module 3: Final Project </strong> </div><ul><li>Final Project: Pre-Work  </li><li>Reading: Final Project Overview  </li><li>Final Project: CodeCraftHub: Building Personalized Learning for Developers  </li></ul><div><strong>Module 4: Final Exam </strong> </div><ul><li>Final Exam: Generative AI for Software Developers  </li></ul><div><strong>Module 5: Course Wrap-Up </strong> </div><ul><li>Reading: Enhancing Your Software Developer Career with Generative AI  </li><li>Video: Wrap-up  </li><li>Reading: Congratulations and Next Steps  </li><li>Reading: Thanks from the Course Team  </li><li>Copyrights and Trademarks  </li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>To get the most out of this course, you should have a working knowledge of Python and JavaScript and familiarity with software development processes.  </div>
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Mastering Generative AI for Software Development

Build job-ready AI skills for software development, automate coding, optimize design, detect bugs, and create AI-powered solutions while considering ethics.  

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  <div>With generative AI transforming the data science landscape, mastering its applications is crucial to data science professionals. This course equips you with cutting-edge generative AI skills tailored to the needs of a data scientist.  <br /><br />Through real-world scenarios, you will explore the use of generative AI throughout the data science lifecycle. Starting with data querying and preparation, you will learn how to leverage generative AI to generate, augment, and refine the data. You will also apply advanced AI techniques to develop and refine machine learning models while exploring ethical implications. <br /><br />This course is designed for existing data scientists, data professionals, analysts, and engineers looking to thrive in the evolving AI landscape.  <br /><br />You’ll gain hands-on experience using generative AI tools, culminating in a course-end project that simulates a real-world scenario. Your project would make an excellent addition to prospective employers. Finally, you’ll also be completing a final quiz to obtain your certificate,  <br /><br />A basic understanding of generative AI, prompt engineering, and data science is essential to complete this course successfully. <br /><br /></div><h1><strong>Course Learning Objectives</strong></h1><ul><li>Leverage various tools like GPT 3.5, ChatCSV, tomat.ai, and so on, available for data scientists working with generative AI for querying and preparing data </li><li>Examine real-world scenarios where generative AI can enhance data science workflows </li><li>Practice generative AI skills in hands-on labs and projects by generating and augmenting datasets for specific use cases </li><li>Apply generative AI techniques in the development and refinement of machine learning models </li></ul><h1>Course Syllabus</h1><div><strong>Module 1: Data Science and Generative AI  </strong></div><ul><li>Video: Generative AI and Data Science  </li><li>Reading: Generative AI Tools for Data Scientists </li><li>Video: Generative AI's Impact Across Industries</li><li>Video: Leveraging Generative AI in Data Science Lifecycle </li><li>Video: Types of Generative AI Models</li><li>Reading: Guide to Choosing a Generative AI Model Type</li><li>Reading: Case Study: Successful Implementation of Generative AI </li><li>Lab: Explore a Simple Generative Tool</li><li>Demo Video: Generative AI for Data Generation and Augmentation</li><li>Lab: Generative AI for Data Generation and Augmentation </li><li>Video: Generative AI for Data Preparation and Data Querying </li><li>Demo Video: Generative AI for Data Preparation </li><li>Lab: Generative AI for Data Preparation </li><li>Demo Video: Generative AI for Querying Databases </li><li>Lab: Generative AI for Querying Databases </li></ul><div><strong>Module 2: Use of Generative AI for Data Science </strong></div><ul><li>Demo Video: Generative AI for Data Insights</li><li>Lab: Generative AI for Data Insights </li><li>Demo Video: Generative AI for Data Visualization</li><li>Lab: Generative AI for Data Visualization </li><li>Video: Generative AI Tools for Model Development</li><li>Lab: Generative AI for Model Development </li><li>Video: Generative AI for Understanding Data and Model Development</li><li>Video: Considerations While Using Generative AI in Industries</li><li>Video: Challenges While Using Generative AI</li><li>Reading: Responsible Generative AI for Data Professionals</li><li>Case Study: Considerations While Using Generative AI in Healthcare</li><li>Lab: Considerations for Data Professionals Using Gen AI</li></ul><div><strong>Module 3: Final Project and Exam</strong></div><ul><li>Reading: Final Project Overview</li><li>Lab: Final Project: Generative AI for Data Science</li><li>Final Exam Instructions</li><li>Final Exam</li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>A basic understanding of generative AI, prompt engineering, and data science is essential to complete this course successfully. </div>
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Mastering Generative AI for Data Science

This course helps you master generative AI skills to enhance data science workflows, improve data preparation, and refine machine learning models with real-world applications.  

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  <div>Imagine you're a real estate analyst examining housing markets across California neighborhoods. Some areas command premium prices while similar-looking houses sell for much less. You begin to wonder: <strong>What makes certain locations more valuable? Which combination of factors truly drives housing prices?</strong> Traditional machine learning models can predict prices accurately, but they rarely explain <strong>why</strong> one neighborhood is worth more than another. This is where <strong>Explainable AI (XAI)</strong> becomes invaluable.</div><div><br />In this hands-on project, you'll use IBM's <strong>AI Explainability 360 toolkit</strong> to develop clear explanations for housing prices. Using <strong>GLRMExplainer (Generalized Linear Rule Models) and LinearRuleRegression</strong>, you'll identify specific rules determining property values and visualize how features influence predictions. By leveraging <strong>interpretable rule-based models</strong>, you'll analyze relationships between <strong>income, house age, location, and other characteristics</strong>, making complex market dynamics understandable. This project demonstrates how explainable AI empowers real estate professionals, policymakers, and homebuyers to <strong>make informed decisions and understand market trends with confidence</strong>.</div><div><br /></div><h1><strong>A Look at the Project Ahead</strong></h1><div>In this project, you'll work with the California Housing dataset to uncover the factors driving property values using intepretable AI models. By the end of the project, you will:</div><ul><li><strong>Preprocess and transform housing data</strong> for rule-based AI analysis</li><li><strong>Build and train interpretable models</strong> using LinearRuleRegression and GLRMExplainer</li><li><strong>Extract and analyze decision rules</strong> to understand key factors influencing housing prices</li><li><strong>Visualize feature contributions</strong> to see how individual characteristics like income, location, and house age impact property values</li><li><strong>Explain specific predictions</strong> for individual properties using transparent rule-based reasoning</li></ul><div><br /></div><h1><strong>What You'll Need</strong></h1><div>To successfully complete this project, you'll need:</div><ul><li>Basic understanding of Python programming and libraries such as pandas, scikit-learn, and matplotlib</li><li>Familiarity with fundamental regression concepts and housing market terminology</li><li>A web browser to access tools and run your code</li></ul><div>By the end of this project, you will have built an AI model that not only predicts housing prices but also <strong>explains the reasoning behind each prediction</strong>, allowing real estate professionals to make data-driven decisions based on transparent, interpretable insights.</div>
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Explainable AI in Housing Markets: Rule-Based Analysis

Get AI to explain what shapes California housing prices. Learn AI Explainability methods which are essential to implementing AI in the regulated industries. As a real estate analyst, explore interpretable AI techniques to reveal why prices vary. Use rule-based models to extract decision rules from housing data, visualizing how income, age, and location influence property values. Turn complex market trends into clear, explainable insights, helping stakeholders make informed decisions with transparent AI-driven analysis instead of black-box predictions. 

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  <div>This course provides the foundational knowledge needed to build Cybersecurity skills as part of the Cybersecurity Analyst Professional Certificate program. </div><div><br />You’ll dive into incident response methodologies and security models, gaining the ability to identify and classify key vulnerabilities and attacks targeting modern organizations. Through detailed analysis of past and recent breaches, you’ll understand how they were detected and what measures could have reduced risk to the organization. Additionally, you’ll examine the financial impact of data breaches through case studies of well-known incidents. <br /><br /></div><div>As part of the course, you'll research a current cybersecurity breach, applying your skills to analyze the attack type, timeline, affected systems, and missed prevention opportunities. Your findings will be evaluated by your peers. </div><div><br />This course is ideal for anyone aiming to pursue a career as a Cybersecurity Analyst or Specialist, and completing it qualifies you to earn the Cybersecurity: Case Studies and Capstone Project digital badge. <br /><br /></div><h1>Learning Outcomes</h1><ul><li>Investigate and explain the concept of a watering hole attack and its impact on organizations. </li><li>Analyze ransomware threats, detailing their potential consequences on organizational security and operations. </li><li>Examine third-party data breaches and assess their implications on an organization's security and business continuity. </li><li>Explore the effects of phishing attacks, including the risks they pose to organizations and their overall impact. </li></ul><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><h1>Recommended Skills Prior to Taking this Course</h1><div>Cybersecurity and Computer Science fundamentals knowledge.  </div>
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Cybersecurity: Case Studies and Capstone Project

Learn essential Cybersecurity skills, including incident response, vulnerability recognition, and breach analysis, with real-world case studies in this course. 

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  <div>This IBM course will equip you with the essential skills required to manage and investigate cybersecurity incidents effectively. You will dive deep into critical topics such as industry-leading incident response frameworks (NIST and SANS), digital forensics methodologies, and best practices for handling and preserving digital evidence. <br /><br /></div><div>Throughout the course, you’ll participate in hands-on labs and real-world projects that will allow you to apply your knowledge in detecting, analyzing, and mitigating cybersecurity threats. You will also gain valuable experience in conducting forensic investigations using industry-standard tools, giving you the practical expertise necessary to respond to cyber incidents swiftly and accurately. <br /><br /></div><div>By the end of this course, you will be prepared to implement incident response plans, collect and analyze digital evidence, and produce detailed reports that support organizational cybersecurity efforts.  <br /><br /></div><div>Whether you’re looking to enhance your existing skills or take your career in cybersecurity to the next level, this course is ideal for learners seeking to build proficiency in digital forensics and incident response. It’s an essential learning experience for anyone aiming to excel in the rapidly growing cybersecurity field. <br /><br /></div><h1>Learning Outcomes</h1><ul><li>Clarify the principles and significance of incident response in cybersecurity. </li><li>Apply techniques to detect and analyze security incidents. </li><li>Utilize methods for collecting and examining digital evidence. </li><li>Evaluate and report digital forensic findings to strengthen cybersecurity initiatives.</li></ul><div><br /></div><h1>Course Syllabus</h1><div>Welcome to the Course</div><ul><li>Video: Course Introduction (3:32)</li><li>Reading: Course Overview</li><li>Plugin/Reading: Helpful Tips for Course Completion</li></ul><div>Module 1: Incident Response Fundamentals</div><ul><li>Module Introduction and Objectives</li><li>Video: What is Incident Response? (5:50)</li><li>Reading: The NIST and SANS Incident Response Frameworks</li><li>Video: Incident Response: Preparation (8:06)</li><li>Reading: The Importance of Incident Response Documentation</li><li>Video: Incident Response: Detection and Analysis (5:37)</li><li>Video: Containment, Eradication, and Recovery (6:01)</li><li>Reading: Incident Response:Tools and Automation</li><li>Video: Post-Incident Activities (5:34)</li><li>Activity: Respond to a Cybersecurity Incident</li><li>Reading: Incident Response and Preparedness Resources</li><li>Practice Quiz: Incident Response Fundamentals</li><li>Reading: Module Summary and Highlights</li><li>Graded Quiz: Incident Response</li></ul><div>Module 2: Digital Forensics and Investigation</div><ul><li>Module Introduction and Objectives</li><li>Video: Digital Forensics (4:27)</li><li>Video: Data Collection and Examination (6:29)</li><li>Lab: Investigate Logs using Cowrie</li><li>Reading: Handling Digital Evidence</li><li>Video: Analysis and Reporting (5:07)</li><li>Video: Forensic Data: Data Files (4:51)</li><li>Reading: Digital Forensics Tools</li><li>Activity: Digital forensics investigation</li><li>Reading: Chances of Recovering Data</li><li>Practice Quiz:  Digital Forensics</li><li>Reading: Module Summary and Highlights</li><li>Graded Quiz: Digital Forensics</li></ul><div>Module 3: Final Project</div><ul><li>Module Objectives</li><li>Glossary: Forensic Analysis and Incident Response</li><li>Reading: Final Project Overview</li><li>Final Project</li><li>Peer Review Assignment</li></ul><div>Course Wrap Up</div><ul><li>Video: Course Wrap Up (2:26)</li><li>Reading: Congratulations and Next Steps</li><li>Reading: Team and Acknowledgments</li><li>Copyrights and Trademarks</li></ul><div>Course Rating and Feedback<br /><br />Badge</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><h1>Recommended Skills Prior to Taking this Course</h1><div>Basic knowledge of networking fundamentals and cybersecurity. </div>
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Cybersecurity: Forensic Analysis and Incident Response

Learn incident response and digital forensics with hands-on labs, covering NIST, SANS frameworks, and best practices for detecting, analyzing, and mitigating cyber threats. 

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  <div>The cost of security breaches continues to rise, prompting businesses to seek skilled professionals who can anticipate and prevent cyber threats. This IBM course in Penetration Testing, Threat Hunting, and Cryptography helps you develop the critical skills needed to identify vulnerabilities, protect assets, and strengthen your organization's cybersecurity defenses using AI and cryptography. <br /><br /></div><div>Throughout the course, you’ll learn the various phases of penetration testing and gain hands-on experience performing software pen testing and scanning code repositories. You’ll also master the creation of detailed penetration testing reports, engage in threat hunting, and integrate AI into threat intelligence processes. In addition, you'll explore Cyber Threat Intelligence (CTI) frameworks to identify and assess potential threats more effectively. <br /><br /></div><div>The course covers essential cryptography techniques, including encryption, decryption, and hashing, while also examining common cryptographic attacks and cryptanalysis methods. By the end, you'll have practical, real-world skills to enhance your organization's security posture. <br /><br /></div><div>To participate in the labs and final project, you will need a laptop or desktop, along with basic knowledge of networking and cybersecurity. This course is ideal for both current cybersecurity professionals looking to sharpen their skills and those aspiring to enter the field.  <br /><br />If you’re ready to gain in-demand cybersecurity expertise in penetration testing, threat hunting, and cryptography, this hands-on course will equip you with the skills needed to advance your career. <br /><br /></div><h1>Learning Outcomes</h1><ul><li>Gain in-demand penetration testing and threat hunting skills, with hands-on practice that employers value on your resume. </li><li>Acquire practical experience applying penetration testing principles using tools like OWASP ZAP and SNYK. </li><li>Learn how to create detailed penetration testing reports and integrate AI for advanced threat hunting and intelligence gathering. </li><li>Master cryptography and cryptanalysis techniques, including encryption and hashing, to ensure data integrity and confidentiality. </li></ul><div><br /></div><h1>Course Syllabus</h1><div>Welcome<br />Video: Course Introduction (4:03)<br />General Information<br />Plugin/Reading: Helpful Tips for Course Completion<br />Grading Scheme<br /><br />Module 1: Penetration Testing Planning and Discovery</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: What is Penetration Testing? (7:40)</li><li>Video: Penetration Testing Phases (7:12)</li><li>Activity: Phases of the Penetration Testing Challenge</li><li>Video: Penetration Testing - Planning Phase (8:29)</li><li>Video: Penetration Testing - Discovery Phase (5:56)</li><li>Reading: Examples of passive and active reconnaissance</li><li>Reading: Google Dorking</li><li>Hands-on Lab: Practicing Google Dorking Commands</li><li>Practice Quiz: Penetration Testing Planning and Discovery</li><li>Reading: Summary and Highlights: Penetration Testing Planning and Discovery</li><li>Module Glossary: Penetration Testing Planning and Discovery Phases</li><li>Graded Assignment: Graded Quiz: Penetration Testing Planning and Discovery</li></ul><div>Module 2: Penetration Testing: Attack Phase</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Penetration Testing: Attack Phase (6:02)</li><li>Reading: Penetration Testing Tools</li><li>Hands-on Lab: Penetration Testing</li><li>Video: Port Scanning (6:52)</li><li>Lab: Port Scanning with NMap</li><li>Lab: Scanning Network Environment with NMap</li><li>Video: Overview Of Network Protocol Analyzers (7:05)</li><li>Hands-on Lab: Network Protocol Analyzers</li><li>Practice Quiz: Penetration Testing: Attack Phase</li><li>Summary and Highlights: Penetration Testing: Attack Phase</li><li>Reading: Glossary:  Penetration Testing: Attack Phase</li><li>Graded Assignment: Graded Quiz: Penetration Testing: Attack Phase</li></ul><div>Module 3: Penetration Testing: Reporting Phase</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Software and Application Pen Testing (6:50)</li><li>Video: Scanning Code Repositories (5:28)</li><li>Hands-on Lab: Getting started with GitHub (optional)</li><li>Hands-on Lab: Using SNYK to scan your code repository (optional)</li><li>Video: Penetration Testing - Reporting Phase (7:35)</li><li>Reading: PTES Framework</li><li>Activity: PTES Framework</li><li>Practice Quiz: Penetration Testing Reporting</li><li>Reading: Summary and Highlights:  Penetration Testing: Reporting Phase</li><li>Plugin/Reading: Glossary:  Penetration Testing: Reporting Phase</li><li>Graded Assignment: Graded Quiz: Penetration Testing: Reporting Phase</li></ul><div>Module 4: Threat Hunting and Threat Intelligence</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Threat Hunting Overview (5:09)</li><li>Video: Threat Intelligence Overview (5:09)</li><li>Reading: Threat Intelligence Process</li><li>Activity: Implement Threat Intelligence</li><li>Reading: Threat Intelligence Sources</li><li>Lab: Review X-Force Exchange Threat Reports</li><li>Video: Security Information and Event Management (7:13)</li><li>Video: AI in Threat Intelligence and Threat Hunting (5:17)</li><li>Reading: User and Entity Behavior Analytics</li><li>Video: Threat Intelligence Framework Overview (7:15)</li><li>Reading: Use Cases of the MITRE ATT&amp;CK Model and the Diamond Model of Intrusion Analysis</li><li>Practice Quiz: Threat Intelligence Platforms and Frameworks</li><li>Reading: Summary and Highlights: Threat Hunting and Threat Intelligence</li><li>Plugin/Reading: Glossary: Threat Hunting and Threat Intelligence</li><li>Graded Assignment: Graded Quiz: Threat Hunting and Threat Intelligence</li></ul><div>Module 5: Cryptography: Principles and Techniques</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Importance of Cryptography (6:12)</li><li>Video:  Overview of Encryption, Decryption, and Hashing (6:32)</li><li>Video: Symmetric and Asymmetric Encryption (6:16)</li><li>Reading: Essentials of Cryptography and Security</li><li>Lab : End of Encryption/ Decryption (RSA Algorithm)</li><li>Video: Common Cryptographic Attacks (6:32)</li><li>Video: Securing SSL/TLS and Strengthening Key Management Edit Set Access Page Actions (4:20)</li><li>Video: Cryptanalysis and Its Impact on Encryption (6:55)</li><li>Reading: Cryptographic Algorithms, Applications, and Attacks</li><li>Lab : Cryptoanalysis Attack</li><li>Reading: Quantum-Safe Cryptography</li><li>Video: IBM Quantum Safe Cryptography Solutions (3:17)</li><li>Practice Quiz: Cryptographic Attacks</li><li>Reading: Summary and Highlights: Cryptography: Principles and Techniques</li><li>Plugin/Reading: Glossary: Cryptography: Principles and Techniques</li><li>Graded Quiz: Cryptography and Cryptographic Attacks</li></ul><div>Module 6: Final Project and Final Quiz</div><ul><li>Module Introduction and Learning Objectives</li><li>Project Overview</li><li>Final Project: Part 1 - Perform Vulnerability Analysis and Penetration Testing</li><li>Final Project: Part 2 - Secure Information Using Symmetric Encryption</li><li>Peer Graded Assignment</li><li>Course Glossary</li><li>Video: Course Highlights and Key Takeaways (7:25)</li></ul><div>Course Wrap-Up</div><ul><li>Reading: Congratulations and Next Steps</li><li>Reading: Team and Acknowledgments</li><li>Copyrights and Trademarks</li></ul><div>Course Rating<br /><br />Claim your Badge</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><h1>Recommended Skills Prior to Taking this Course</h1><div>A basic understanding of IT systems and cybersecurity principles is recommended prior to enrollment. </div>
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Cybersecurity: Pen-Testing, Threat Hunting, & Encryption

Build in-demand cybersecurity skills in penetration testing, threat hunting, and cryptography. Learn to prevent cyber threats, create reports, and use AI for threat intelligence. 

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  <div>In today’s rapidly evolving digital landscape, where cybersecurity threats are increasingly sophisticated, the demand for skilled cybersecurity professionals continues to rise globally. With more talented candidates entering the field, standing out in this competitive job market can be challenging. So, how can you gain the edge needed to secure your place? <br /><br /></div><div>This course is designed to prepare you for success in landing your first cybersecurity job. Whether you're new to the field or looking to strengthen your application, this course will equip you with the practical skills and knowledge needed to become a top candidate. You’ll learn how to create essential job application materials such as resumes, portfolios, cover letters, and elevator pitches that highlight your strengths and make you an attractive hire. <br /><br /></div><div>You'll gain critical insights into how to effectively search for job opportunities, evaluate job listings, and apply strategically. The course also guides you through the interview preparation process, helping you understand the most common cybersecurity interview questions and providing mock interview scenarios to practice your responses. <br /><br /></div><div>Throughout this four-module, self-paced course, you’ll hear from seasoned cybersecurity professionals who share their personal experiences, advice on networking, and strategies for succeeding in coding tests and interview questions. By the end of the course, you’ll not only be equipped to apply confidently for entry-level cybersecurity positions but also be prepared to tackle real-world challenges in the interview process. <br /><br /></div><div>Join us on this journey to launch your cybersecurity career with the knowledge and tools that will set you apart from the competition!<br /><br /></div><h1>Learning Outcomes</h1><ul><li>Describe cybersecurity roles, job titles, responsibilities, and the key skills and competencies needed, while also exploring the NICE Framework. </li><li>Develop a resume, portfolio, elevator pitch, and cover letter, incorporating best practices to effectively prepare for a cybersecurity job interview. </li><li>Prepare for and respond to a variety of cybersecurity interview questions, including those about industry knowledge, technical skills, experience, and behavioral traits. </li><li>Outline the necessary steps to take after an interview to enhance your chances of success, and apply strategies for negotiating salary effectively<strong>.</strong> </li></ul><h1>Course Syllabus</h1><div><strong>Welcome</strong></div><ul><li>Video: Course Introduction (03:38)</li><li>Reading: General Information</li><li>Reading: Learning Objectives and Course Syllabus</li><li>General Information</li><li>Reading: Grading Scheme</li><li>Reading: Helpful Tips for Course Completion</li></ul><div><strong>Module 1: Getting Ready for a Cybersecurity Career </strong></div><ul><li>Reading: Module Introduction and Learning Objectives </li><li>Video: The Role of the Cybersecurity Professional (05:57)</li><li>Video: Expert Viewpoints: Meet the Experts (02:21)</li><li>Video: Expert Viewpoints: Cybersecurity Roles and Skills (05:05)</li><li>Lab: Perform Cybersecurity Skills' Readiness Check</li><li>Video: IBM Cybersecurity Professional Certificates (06:58)</li><li>Reading: Additional Program Recommendations</li><li>Video: Cybersecurity Career Activities (05:45)</li><li>Video: Expert Viewpoints: Cybersecurity as a Career (04:55)</li><li>Reading: Career Pathways: NICE Framework</li><li>Reading: Purpose and Value of a Professional Portfolio</li><li>Video: Illustrating a Cybersecurity Professional's Portfolio (05:37)</li><li>Video: Expert Viewpoints: Professional Portfolios (06:54)</li><li>Lab: Develop a Professional Portfolio</li><li>Video: Creating Profiles for Online Sites (04:56)</li><li>Video: Drafting the Elevator Pitch (06:22)</li><li>Video: Expert Viewpoints: Elevator Pitch (06:10)</li><li>Lab: Develop an Elevator Pitch</li><li>Reading: Module 1 Summary: Getting Ready for a Cybersecurity Career</li><li>Practice Quiz: Getting Ready for a Cybersecurity Career</li><li>Reading: Module 1 Glossary: Getting Ready for a Cybersecurity Career</li><li>Module 1 Graded Quiz: Getting Ready for a Cybersecurity Career</li></ul><div><strong>Module 2: Identifying Jobs, Creating Resumes, and Drafting Cover Letters </strong></div><ul><li>Reading: Module Introduction and Learning Objectives</li><li>Video: Conducting Company and Industry Research (06:06)</li><li>Reading: Finding Job Postings</li><li>Lab: Assess Job Postings</li><li>Video: Expert Viewpoints: Conducting Company Research (06:41)</li><li>Reading: Online and Offline Networking</li><li>Video: Working with Third-Party Recruiters (07:06)</li><li>Video: Drafting a Resume (07:02)</li><li>Reading: Gathering Professional References</li><li>Video: Expert Viewpoints: Developing a Basic Resume (03:04)</li><li>Lab: Draft a Basic Resume</li><li>Video: Drafting a Cover Letter (04:39)</li><li>Lab: Draft a Cover Letter</li><li>Reading: Module Summary: Identifying Jobs, Creating Resumes, and Drafting Cover Letters</li><li>Practice Quiz:  Identifying Jobs, Creating Resumes, and Drafting Cover Letters </li><li>Reading: Module 2 Glossary: Identifying Jobs, Creating Resumes, and Drafting Cover Letters</li><li>Module 2 Graded Quiz: Identifying Jobs, Creating Resumes, and Drafting Cover Letters</li></ul><div><strong>Module 3: The Interview Process </strong></div><ul><li>Reading: Module Introduction and Learning Objectives</li><li>Video: The End-to-End Interview Process (03:35)</li><li>Video: Submitting Your Application (06:02)</li><li>Video: Preparing for the Interview (05:51)</li><li>Reading: Human Resources (HR) Screening</li><li>Reading: First Stage Interview Process</li><li>Video: Expert Viewpoints: First Round Interview (03:22)</li><li>Video: Cybersecurity Specialist Mock Interview: First Stage (08:16)</li><li>Reading: Technical Screening</li><li>Video: Cybersecurity Specialist Mock Interview: The Technical Interview (08:33)</li><li>Lab: Develop Responses to Common Cybersecurity Specialist Interview Questions</li><li>Reading: Final Interview Checklist</li><li>Video: Final Interview: Pre-Interview Steps (05:18)</li><li>Reading: The Remote or Virtual Interview</li><li>Video: Expert Viewpoints: Interview Preparation (04:59)</li><li>Video: Attending the Interview (04:48)</li><li>Video: Answering Questions (05:37)</li><li>Video: Expert Viewpoints: Responding to Questions (03:01)</li><li>Reading: Closing the Interview</li><li>Video: Cybersecurity Specialist Mock Interview: Final Round (09:35)</li><li>Lab: Respond to Questions Using the STAR Technique</li><li>Reading: Module Summary: The Interview Process</li><li>Practice Quiz: The Interview Process</li><li>Reading: Module 3 Glossary: The Interview Process</li><li>Module 3 Graded Quiz: The Interview Process </li></ul><div><strong>Module 4: Understanding Interview Questions and Post-Interview Next Steps </strong></div><ul><li>Reading: Module Introduction and Learning Objectives</li><li>Video: Core and Behavioral Interview Questions (06:23)</li><li>Reading: Case Study Questions</li><li>Video: Expert Viewpoints: Interview Questions (05:41)</li><li>Lab: Critique Cybersecurity Specialist Mock Interview Responses</li><li>Reading: Other Interview Methods</li><li>Video: Trick Questions and Illegal Questions (05:08)</li><li>Video: Expert Viewpoints: Difficult Questions (04:09)</li><li>Reading: Post-Interview Checklists</li><li>Reading: If You Get an Offer</li><li>Video: Salary Negotiation (05:07)</li><li>Reading: If You Don’t Get an Offer</li><li>Video: Expert Viewpoints: Job Offers (03:54)</li><li>Reading: Final Course Checklist</li><li>Reading: Module Summary: Understanding Interview Questions and Post-Interview Next Steps</li><li>Practice Quiz: Understanding Interview Questions and Post-Interview Next Steps</li><li>Reading: Navigating the Future of Cybersecurity: Key Trends and How to Stay Ahead</li><li>Reading: Module 4 Glossary: Understanding Interview Questions and Post-Interview Next Steps</li><li> Module 4 Graded Quiz: Understanding Interview Questions and Post-Interview Next Steps </li></ul><div><strong>Course Wrap-Up</strong></div><ul><li>Reading: Course Summary</li><li>Reading: Congratulations and Next Steps</li><li>Reading: Thanks from the Course Team</li><li>Copyright and Trademarks</li></ul><div><strong>Course Rating and Feedback<br /></strong><br /></div><div><strong>Badge</strong><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><h1>Recommended Skills Prior to Taking this Course</h1><div>Completion of prior courses in the Cybersecurity Analyst and a basic understanding of IT systems and cybersecurity principles is recommended prior to enrollment. </div>
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Cybersecurity Job Guide: Resume & Interview Preparation

Boost your cybersecurity career with this course. Learn to craft resumes, cover letters, and ace interviews, gaining insights from experts to stand out in a competitive field.

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  <div>This comprehensive course is designed to help you prepare for the CompTIA Security+ and CySA+ certifications, two key credentials for advancing in the cybersecurity field. Split into two focused modules, the course covers the essential exam details, eligibility requirements, and sign-up procedures for both certifications. You'll explore the five domains for Security+ and the four domains for CySA+, learning how to allocate study time effectively based on the question distribution for each domain. <br /><br /></div><div>With practical study tips, additional resource recommendations, and testing strategies, you’ll be fully equipped to tackle the exams. The course includes ungraded practice tests to assess your readiness, along with timed mock exams to simulate real testing conditions. By the end of the course, you’ll have the confidence and skills needed to excel in both CompTIA exams. <br /><br /></div><div>Plus, take advantage of exclusive IBM discounts on CompTIA content, exam vouchers, and bootcamps to further support your cybersecurity career journey. <br /><br /></div><h1>Learning Outcomes:</h1><ul><li>Explain the CompTIA certification sign-up process, requirements, and exam costs for Security+ and CYSA+. </li><li>Identify the domains and question distribution for each CompTIA Security+ and CYSA+ exam. </li><li>Find additional study resources tailored for the CompTIA Security+ and CYSA+ certifications. </li><li>Assess your exam readiness with practice tests and prepare for the test day with mock, timed exams. </li></ul><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><h1>Recommended Skills Prior to Taking this Course</h1><div>Completion of prior courses in the Cybersecurity Analyst Professional Certificate is strongly suggested. </div>
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Practice Exams for CompTIA Security+ & CySA Certifications

Master CompTIA Security+ and CySA+ exams in this 2-module course, with study tips, practice tests, and exam prep strategies to boost your cybersecurity career.

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  <div>Jakarta Faces is a framework for building component-based web applications that simplifies user interface (UI) development by managing reusable components, handling user interactions, and binding data to backend logic. It provides built-in lifecycle management, event handling, and server-side validation, reducing the need for manual request processing. Jakarta Faces also includes tag libraries that allows developers define UI components using markup and connect them to backend objects without writing repetitive setup code.<br /><br />To further streamline development, Jakarta Faces works with Jakarta Contexts and Dependency Injection (CDI)   to manage backend components. CDI allows beans to be automatically created and injected where needed, making it easier to manage application logic. Jakarta Expression Language enables data binding between the UI and backend, allowing UI components to dynamically display data and trigger backend actions.<br /><br />You’ll learn how to build a dynamic web application using Jakarta Faces for UI, CDI for managing backend logic, and Jakarta Expression Language (EL) for data binding.<br /><br /><br /></div>
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Building a dynamic web app with integrated UI and backend

Learn how to build a dynamic web application using Jakarta Faces, Jakarta Contexts and Dependency Injection, and Jakarta Expression Language.

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  <div>Cyberthreats are a growing challenge, costing billions and impacting everyone, from governments to small businesses. Are you ready to be part of the solution? <br /><br />This course offers a comprehensive understanding of cybersecurity compliance principles, industry standards, regulations, and audit processes. You’ll explore essential topics such as governance, risk management, compliance, cybersecurity frameworks, and process management. <br /><br /></div><div>You’ll dive into IT service management, the NIST Risk Management Framework, and AI ethical considerations. The course also covers cybersecurity laws and regulations, including HIPAA, GDPR, and PCI DSS, from both US and global perspectives. Additionally, you’ll learn about audit processes using the COBIT framework and SOC reports, and explore key standards like OWASP, ISO, and IEEE, with practical guidance on applying them. <br /><br /></div><div>Over five self-paced modules, you’ll engage in interactive activities to apply your knowledge in real-world scenarios and complete a final project to demonstrate your skills and understanding. <br /><br /></div><h1>Learning Outcomes</h1><ul><li>Understand the core concepts of cybersecurity, including governance, risk management, compliance, AI ethics, and the audit process. </li><li>Identify key cybersecurity laws and regulations at both national and international levels, and understand their impact on organizations. </li><li>Explore industry standards and their role in cybersecurity practices, with a focus on the COBIT framework and SOC reports. </li><li>Apply cybersecurity standards and best practices to reduce risks, strengthen security, and ensure compliance through effective audit processes.</li></ul><h1>Course Syllabus</h1><div><strong>Welcome</strong></div><ul><li>Video: Course Introduction (3:49)</li><li>General Information</li><li>Learning Objectives and Syllabus</li><li>Grading Scheme</li></ul><div><strong>Module 1: Introduction to Information Security and Compliance</strong></div><ul><li>Module Introduction and Learning Objectives </li><li>Video: Governance, Risk, and Compliance (GRC) </li><li>Video: Governance Components </li><li>Reading: GRC Tools </li><li>(Lab) Match Activity: Applying GRC Concepts </li><li>Video: NIST Cybersecurity Framework </li><li>Reading: Aligning to the NIST Cybersecurity Framework  </li><li>Activity: Align to the NIST Cybersecurity Framework </li><li>Video: Elements of Effective Security Compliance </li><li>Video: Standardize Processes   </li><li>Video: Automation and Orchestration </li><li>Video: Change Management </li><li>Reading: Asset Management </li><li>Module 1 Summary: Introduction to Information Security and Compliance </li><li>Module 1 Glossary: Introduction to Information Security and Compliance </li><li>Module 1 Graded Quiz: Introduction to Information Security and Compliance </li><li>Discussion Prompt: (Optional) CSF Implementation </li></ul><div><strong>Module 2: Foundations of IT Service Management and Risk Governance</strong> </div><ul><li>Module Introduction and Learning Objectives </li><li>Video: Information Technology Infrastructure Library (ITIL) Overview </li><li>Video: Key ITIL Processes  </li><li>Activity: Identify the ITIL Phases </li><li>Reading: ITIL Process Implementation </li><li>Video: Risk Management </li><li>Reading: Risk Analysis </li><li>(Lab) Matching: Accept, Transfer, Avoid, or Mitigate?  </li><li>Video: Third-Party Risk Assessment and Management </li><li>Video: AI Ethics  </li><li>Reading: EU AI Act  </li><li>Video: How to Implement AI Ethics  </li><li>Activity: Apply AI Ethics </li><li>Module 2 Summary: Foundations of IT Service Management and Risk Governance  </li><li>Module 2 Glossary: Foundations of IT Service Management and Risk Governance  </li><li>Module 2 Graded Quiz: Foundations of IT Service Management and Risk Governance  </li><li>Discussion Prompt:(Optional) Foundations of IT Service Management and Risk Governance </li></ul><div><strong>Module 3: Understanding Cybersecurity Laws and Regulations </strong></div><ul><li>Module Introduction and Learning Objectives </li><li>Video: Overview of US Cybersecurity Laws and Regulations  </li><li>Video: HIPAA Security Rule  </li><li>Reading: HIPAA Cybersecurity Guidance </li><li>Activity: Apply HIPAA Guidelines </li><li>Reading: US Laws and Regulations Cheat Sheet </li><li>Video: Overview of Global Cybersecurity Laws and Regulations  </li><li>Reading: Applying Global Cybersecurity Laws and Regulations </li><li>Activity: Apply global cybersecurity laws and regulations </li><li>Reading: Global Cybersecurity Laws and Regulations Cheat Sheet </li><li>Practice Quiz: Understanding Cybersecurity Laws and Regulations  </li><li>Module 3 Summary:  Understanding Cybersecurity Laws and Regulations  </li><li>Module 3 Glossary:  Understanding Cybersecurity Laws and Regulations  </li><li>Module 3 Graded Quiz:  Understanding Cybersecurity Laws and Regulations  </li><li>Discussion Prompt: (Optional) Cybersecurity Laws</li></ul><div><strong>Module 4: Understanding Cybersecurity Standards and Audits </strong></div><ul><li>Module Introduction and Learning Objectives </li><li>Video: Industry Standards  </li><li>Reading: OWASP Use Cases </li><li>Activity: Apply OWASP Standards </li><li>Video: NIST, ISO, and IEEE Overview </li><li>Reading: NIST Standards  </li><li>Reading: ISO Standards  </li><li>Reading: IEEE Standards </li><li>Video: Security Control Audits  </li><li>Video: Performing a Security Audit </li><li>Video: ISACA Standards </li><li>Reading: COBIT </li><li>Video: SOC Reports  </li><li>Practice Quiz: Understanding Cybersecurity Standards and Audits</li><li>Module 4 Summary: Understanding Cybersecurity Standards and Audits </li><li>Module 4 Glossary: Understanding Cybersecurity Standards and Audits </li><li>Module 4 Graded Quiz: Understanding Cybersecurity Standards and Audits </li><li>Discussion Prompt: (Optional) Conducting Security Audits </li></ul><div><strong>Module 5: Final Project</strong></div><ul><li>Module Introduction and Learning Objectives </li><li>Final Project Overview </li><li>Final Project Scenarios </li><li>Final Project  </li></ul><div><strong>Course Wrap-Up</strong></div><ul><li>Video: Course Summary</li><li>Congratulations and Next Steps</li><li>Thanks from the Course Team </li><li>Copyrights and Trademarks </li></ul><div><strong>Course Rating &amp; Feedback<br /></strong><br /></div><div><strong>Badge<br /></strong><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><h1>Recommended Skills Prior to Taking this Course</h1><div>Knowledge of cybersecurity and IT fundamentals. </div>
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Cybersecurity: Framework, Compliance, and Governance

Learn cybersecurity principles, industry standards, regulations, and audit processes. Explore key concepts in information security, compliance, and risk management. 

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  <div>As cyberattacks increasingly target databases and the cost of a data breach continues to rise, businesses are urgently seeking skilled cybersecurity professionals to safeguard their data. This course provides you with essential database security skills that are in high demand. <br /><br />You'll learn key database concepts, such as relational and non-relational databases, and master SQL fundamentals. You'll gain hands-on experience creating and executing SQL queries while building foundational knowledge in relational data management, roles, and permissions. <br /><br /></div><div>The course covers vital information security measures, including encryption, hashing, masking, tokenization, and access restrictions to protect sensitive data.  <br /><br /></div><div>You will learn to manage database user profiles, implement password policies, assign privileges, and design and implement robust security and auditing models. <br /><br /></div><div>Additionally, the course addresses database injection vulnerabilities like OS command and SQL injection attacks, providing strategies for mitigating these risks. <br /><br /></div><div>With practical labs focused on user management and access control, this course will help you develop critical database security skills that will make you attractive to potential employers. </div><div><br /></div><h1>Learning Outcomes</h1><ul><li>Master the key data management skills needed for job readiness, with a focus on data protection techniques to safeguard sensitive information. </li><li>Understand relational database (RDBMS) fundamentals and practice querying databases with SQL. </li><li>Learn how to configure and manage database user profiles, implement password policies, and assign roles and privileges effectively. </li><li>Identify potential vulnerabilities in databases, such as OS command and SQL injection attacks, and gain strategies to defend against these threats. </li><li>Apply data protection practices to ensure security and transparency in data management processes. </li></ul><h1>Course Syllabus</h1><div>Welcome<br />Video: Course Introduction (4:01)<br />General Information<br />Learning Objectives and Syllabus<br />Grading Scheme<br /><br />Module 1: Database Fundamentals</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Introduction to Data and Databases (2:56)</li><li>Video: Relational Databases (7:34)</li><li>Video: Non-Relational Databases (7:33)</li><li>Video: Relational Data Concepts (5:34)</li><li>Lab: Relational Data Concepts</li><li>Practice Quiz: Database Fundamentals</li><li>Module 1 Summary: Database Fundamentals</li><li>Module 1 Graded Quiz: Database Fundamentals</li><li>(Optional) Discussion Prompt: Relational and non-relational databases</li></ul><div>Module 2: SQL Basics and Database Management Fundamentals</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: SELECT Statement (3:41)</li><li>Reading: SELECT statement examples</li><li>Lab: Simple SELECT Statements</li><li>Video: COUNT, DISTINCT, and LIMIT (2:31)</li><li>Lab: COUNT, DISTINCT, and LIMIT</li><li>Video: INSERT Statement (2:42)</li><li>Video: UPDATE and DELETE Statements (3:17)</li><li>Lab: INSERT, UPDATE, and DELETE</li><li>Video: Database Roles and Permissions (5:29)</li><li>Video: Database Management (6:56)</li><li>Video: Backing Up Databases (5:39)</li><li>Lab: Performing Basic Database Operations</li><li>Practice Quiz: SQL Basics and Database Management Fundamentals</li><li>Module 2 Summary: SQL Basics and Database Management Fundamentals</li><li>Module 2 Graded Quiz: SQL Basics and Database Management Fundamentals</li><li>Discussion Prompt: SQL Statements</li></ul><div>Module 3: Database Security and Data Protection Strategies</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Data Types (7:47)</li><li>Video: Data Classifications (7:22)</li><li>Video: General Data Protection Considerations (6:33)</li><li>Video: Methods to Securing Data: Part 1 (0:00)</li><li>Video: Methods to Securing Data: Part 2 (6:53)</li><li>Lab: Securing Data</li><li>Video: Database User Profiles, Password Policies, Privileges, and Roles (6:57)</li><li>Video: Design and Implement Database Application Security Models (7:25)</li><li>Video: Design and Implement Database Auditing Models (7:23)</li><li>Video: Application Data Auditing (6:13)</li><li>Video: Auditing Database Activities (7:08)</li><li>Lab: Managing MySQL User Accounts, Access Control, and Encryption</li><li>Reading: Case Study: Data Breach</li><li>Practice Quiz: Database Security and Data Protection Strategies</li><li>Module 3 Summary: Database Security and Data Protection Strategies</li><li>Module 3 Graded Quiz: Database Security and Data Protection Strategies</li><li>Discussion Prompt (Optional): Data Protection Methods</li></ul><div>Module 4: Injection Vulnerabilities</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Introduction to Injection Flaws (2:29)</li><li>Video: OS Command Injection: Part 1 (5:37)</li><li>Video: OS Command Injection: Part 2 (5:16)</li><li>Video: OS Command Injection: Part 3 (6:02)</li><li>Video: SQL Injection: Part 1 (7:34)</li><li>Video: SQL Injection: Part 2 (5:21)</li><li>Video: Database Injection Vulnerabilities (6:31)</li><li>Video: Other Types of Injections (4:14)</li><li>Lab: Exploiting SQL Injection Vulnerabilities</li><li>Reading: Additional Resources</li><li>Practice Quiz: Injections Vulnerabilities</li><li>Module 4 Summary: Injection Vulnerabilities</li><li>Module 4 Graded Quiz: Injection Vulnerabilities</li><li>(Optional) Discussion Prompt: Mitigating Injection Flaws</li></ul><div>Module 5: Final Assessment and Final Project</div><ul><li>Module Introduction and Learning Objectives</li><li>Reading: Final Project Overview</li><li>Lab: Final Project: Comprehensive Database Security and Management</li><li>Peer-Graded Assignment: Comprehensive Database Security and Management</li><li>Module 5 Graded Quiz: Database Basics and Security</li><li>Course Glossary: Database Fundamentals and Vulnerabilities</li></ul><div>Course Wrap-Up</div><ul><li>Video: Course Wrap-Up (4:18)</li><li>Reading: Congratulations and Next Steps</li><li>Reading: Thanks from the Course Team</li><li>Copyrights and Trademarks</li></ul><div>Course Rating<br /><br />Digital Badge<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>
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Database Fundamentals and Vulnerabilities

Learn how to secure data, manage database users and permissions, and prevent injection vulnerabilities to protect databases from expensive breaches, while gaining valuable cybersecurity skills.

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  <div>The demand for information security analysts is growing as companies seek professionals to protect their networks. This course offers practical, hands-on experience in computer networking and network security, equipping you with the skills employers need. Through interactive labs, you’ll learn to secure small office/home office (SOHO) networks, configure DHCP, filter DNS, and use open-source Extended Detection and Response (XDR) systems. </div><div><br />You’ll also gain a solid understanding of network fundamentals such as ports, protocols, IP addresses (including IPv6), and network routing. Learn about Layer 2 and 3 addressing, routers, and routing tables, which are essential for effective network management. </div><div><br />In addition, you’ll explore cybersecurity tools for data protection, endpoint security, and SIEM, preparing you to meet an organization’s compliance and threat intelligence needs. This knowledge is vital in today’s fast-evolving cybersecurity environment. <br /><br /></div><div>You’ll complete a final project that demonstrates your ability to handle network and security planning tasks. <br /><br /></div><div>Invest in your future and gain the networking and security expertise that employers are looking for—enroll today! <br /><br /></div><h1>Learning Outcomes</h1><ul><li>Identify key computer network and network security skills needed to protect networks and endpoint devices from unauthorized access. </li><li>Apply organizational policies for endpoint device protection using firewall configurations and inbound/outbound rulesets. </li><li>Install and configure security tools, apply patches, and use cloud-based software for detecting, quarantining, and removing malware, as well as managing intrusion detection systems. </li><li>Recommend network hardware, security solutions, and monitoring practices to enhance network security and management. </li></ul><div><br /></div><h1>Course Syllabus</h1><div><strong>Module 1 – Networking Fundamentals </strong></div><ul><li>Module Introduction and Learning Objectives </li><li>Computer Networks: Networking Hardware Devices  </li><li>Models, Standards, Protocols, and Ports </li><li>Computer Networks: Wireless Networks and Standards  </li><li>Activity: Identifying Wireless Network Technologies </li><li>Protocol Table </li><li>Hands-On-Lab: Exploring Networking Ports and Protocols </li><li>Reading: Designing a Basic Network Structure </li><li>Hands-On-Lab: Designing a Basic Network Structure </li><li>Hands-On-Lab: Ports and Protocols in Windows Defender </li><li>Practice Quiz: Networking Fundamentals </li><li>Summary </li><li>Module 1 Glossary </li><li>Module 1 Graded Quiz </li><li>Discussion Prompt: Design a network structure for the future </li></ul><div><strong>Module 2 – IP Addressing Routing and Switching </strong></div><ul><li>Basics of IP Addressing </li><li>Module Introduction and Learning Objectives </li><li>IP Addressing - The Basics of Binary  </li><li>IP Address Structure and Network Classes  </li><li>IP Protocol and Traffic Routing  </li><li>Introduction to the IPv6 Address Schema  </li><li>Hands-On-Lab: IP Addressing and Subnet Masking </li><li>Basics of Network Routing </li><li>Layer 2 and Layer 3  Addressing  </li><li>Address Resolution Protocol  </li><li>Routers and Routing Tables Part 1 </li><li>Routers and Routing Tables, Part 2 </li><li>Routers and Routing Tables, Part 3  </li><li>Hands-On-Lab: Securing a SOHO Network  </li><li>Practice Quiz: IP Addressing Routing and Switching </li><li>Summary </li><li>Module 2 Glossary </li><li>Module 2 Graded Quiz </li></ul><div><strong>Module 3: Network Protocols </strong></div><ul><li>Module Introduction and Learning Objectives </li><li>Application and Transport Protocols Part 1  </li><li>Application and Transport Protocols Part 2  </li><li>Hands-On-Lab: Analyzing TCP and UDP Traffic with Wireshark </li><li>Hands-On-Lab: Protecting Endpoints with OpenEDR </li><li>DNS and DHCP  </li><li>Hands-On-Lab: Installing and Configuring DHCP  </li><li>DNS Filtering  </li><li>Hands-On-Lab: DNS Filtering </li><li>Syslog Message Logging Protocol  </li><li>Flows and Network Analysis  </li><li>Port Mirroring and Promiscuous Mode  </li><li>User Behavior Analysis  </li><li>Practice Quiz: Network Protocols </li><li>Summary </li><li>Module 3 Glossary </li><li>Module 3 Graded Quiz </li><li>Discussion Prompt: Design a network structure for the future </li></ul><div><strong>Module 4: Network Security </strong></div><ul><li>Stateless Inspection  </li><li>Stateful Inspection  </li><li>Firewall Filters- IDS and IPS Systems  </li><li>Hands-On-Lab: Protocols and Ports in Windows Defender </li><li>Hands-on Lab: Windows Firewall with Advanced Security </li><li>Using Your Microsoft Windows Lab Workspace </li><li>Hands-On-Lab: Configuring Firewalls in Microsoft Windows </li><li>Intrusion Detection and Prevention Systems Compared </li><li>Network Address Translation </li><li>Hands-On-Lab: Intrusion Detection System Activity </li><li>File Integrity Monitoring  </li><li>Data Loss Prevention (DLP)  </li><li>Network Access Control (C) </li><li>Endpoint Detection and Response (EDR)  </li><li>Extended Detection and Response (XDR)  </li><li>Practice Quiz: Network Security Techniques </li><li>Summary </li><li>Module 4 Glossary </li><li>Module 4 Graded Quiz </li></ul><div><strong>Module 5: Final Project  </strong></div><ul><li>Final Project Overview  </li><li>Final Project: Network Design and Security Configuration  </li><li>Peer-Graded Final Assignment </li></ul><div><strong>Course Rating and Feedback </strong></div><ul><li>Course Rating and Feedback </li></ul><div><strong>Badge </strong></div><ul><li>Badge Frequently Asked </li><li>Claim your badge here </li></ul><div><strong>Course Wrap Up </strong></div><ul><li>Course Wrap Up </li><li>Congratulations and Next Steps </li><li>Course Team and Acknowledgements </li><li>Copyright and Trademarks </li></ul><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>
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Network Fundamentals and Security

This course equips you with in-demand networking and network security skills, including configuring settings such as DHCP, securing networks, and using network diagnostic and protection tools. 

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  <div>Many organizations are increasing their security investments due to recent breaches, highlighting the growing demand for skilled IT support and cybersecurity professionals. Whether you’re new to tech or transitioning into a new role, this course offers the critical operating systems (OS) knowledge you need to advance your career. <br /><br />Operating Systems are foundational to all aspects of computing. In this course, you’ll explore key concepts shared across all OSes and dive into the unique features of Windows, Linux, and macOS. <br /><br /></div><div>You’ll learn about OS file systems and their security features, discover the innovations introduced by macOS, and explore the concepts of virtualization and containerization. Through hands-on labs and a final project, you’ll practice managing virus protection, configuring user accounts and permissions using command-line tools, and securing systems against various threats. <br /><br /></div><div>If you're looking to start a career in IT or cybersecurity, enroll today to gain essential OS knowledge that will help you move forward in roles such as cybersecurity, system administration, network engineering, or IT support. <br /><br /></div><h1>Learning Outcomes</h1><ul><li>Boost your front-end development career by creating interactive web applications with JavaScript technologies like React, JSX, and ES6.</li><li>Quickly build dynamic client-side apps using reusable React components.</li><li>Enhance the flexibility and scalability of your components through features such as props, state, hooks, forms, and Redux.</li><li>Showcase your React skills by developing multiple front-end applications, such as a shopping cart.</li></ul><h1>Course Syllabus</h1><div>Welcome </div><ul><li>Video: Course Introduction (3:40)</li><li>General Information</li><li>Learning Objectives and Syllabus</li><li>Grading Scheme</li></ul><div>Module 1: Windows Operating Systems</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: An Introduction to Operating Systems (7:48)</li><li>Video: Microsoft Windows Systems Overview (7:08)</li><li>Video: File Systems (5:19)</li><li>Video: Directory Structure (7:14)</li><li>Reading: File Naming Best Practices</li><li>Video: Using Your Windows Lab Workspace (3:51)</li><li>Lab: Using File Explorer to Manage Files and Folders'</li><li>Video: User and Kernel Modes (5:04)</li><li>Video: Microsoft Windows Server Overview (8:00)</li><li>Video: Using Your Microsoft Windows Lab Workspace (3:51)</li><li>Lab: Exploring Microsoft Windows Server Features</li><li>Video: Overview of Microsoft Windows Command Prompt Tools for Administration (6:19)</li><li>Reading: Using Microsoft Windows Command Prompt Tools for Administration</li><li>Lab: Use the Windows Command Prompt for Administration</li><li>Video: Microsoft Management Console (4:41)</li><li>Reading: Manage User Accounts</li><li>Lab: Creating Windows Users and Groups</li><li>Video: What is Active Directory? (4:42)</li><li>Module Summary: Microsoft Windows Operating System</li><li>Practice Quiz: Microsoft Windows Operating System</li><li>Module 1 Glossary: Microsoft Windows Operating System</li><li>Graded Quiz: Microsoft Windows Operating System</li></ul><div>Module 2: Windows Security</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Active Directory Accounts and Security Considerations (6:18)</li><li>Reading: Authentication Methods</li><li>Video: Windows Security Settings (3:50)</li><li>Video: User Management and Permissions (5:37)</li><li>Video: Antimalware Tools and Microsoft Windows Firewall Configuration (7:59)</li><li>Lab: Windows Defender Firewall</li><li>Lab: Creating a Firewall Rule in Microsoft Windows Defender</li><li>Video: Overview of Patching (6:03)</li><li>Video: Microsoft Update (5:50)</li><li>Video: Kerberos (7:14)</li><li>Video: Overview of Windows Auditing (5:38)</li><li>Module Summary: Windows Security</li><li>Practice Quiz: Windows Security</li><li>Module 2 Glossary: Windows Security</li><li>Graded Quiz : Windows Security</li></ul><div>Module 3: Linux Operating Systems</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Linux Systems Overview (5:11)</li><li>Video: Exploring Linux Operating System Essentials (6:49)</li><li>Video: Linux Terminal Overview (6:11)</li><li>Lab: Getting Started with Linux Terminal</li><li>Reading: Linux Commands</li><li>Video: Common Linux/Unix Shell Commands (6:11)</li><li>Lab: Common Linux/Unix Commands</li><li>Reading: Linux and Bash Command Cheat Sheet: The Basics</li><li>Lab: Linux User Management</li><li>Video: Linux File Systems and Directory Structure (5:22)</li><li>Reading: Linux Run Levels</li><li>Reading: Securing Linux</li><li>Lab: Updating Linux</li><li>Module Summary: Linux Operating Systems</li><li>Practice Quiz: Linux Operating Systems</li><li>Module 3 Glossary: Linux Operating Systems</li><li>Graded Quiz: Linux Operating Systems</li></ul><div><br />Module 4: macOS and Virtualization</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: macOS Overview (6:48)</li><li>Video: macOS Features and Tools (5:09)</li><li>Activity: Explore macOS Features</li><li>Reading: Upgrading macOS</li><li>Video: macOS System Preferences (2:52)</li><li>Reading: Updates for macOS</li><li>Video: Introduction to Mobile Devices: iOS Overview (7:53)</li><li>Video: Introduction to Mobile Devices: Android Overview (8:08)</li><li>Video: An overview of Virtualization (7:37)</li><li>Activity: Create a Virtual Machine Simulation</li><li>Video: Overview of Cloud Computing (6:39)</li><li>Reading: Differences Between Virtualization and Cloud Computing</li><li>Video: Introduction to Containers (6:13)</li><li>Reading: Differences Between Containers and Virtual Machines</li><li>Video: Introduction to Docker (3:36)</li><li>Lab: Introduction to Containers, Docker, and IBM Cloud Container Registry</li><li>Module Summary: macOS and Virtualization</li><li>Practice Quiz: macOS and Virtualization</li><li>Module 4 Glossary: macOS and Virtualization</li><li>Graded Quiz: macOS and Virtualization</li></ul><div>Module 5: Final Project</div><ul><li>Learning Objectives</li><li>Reading: Final Project Overview</li><li>Video: Using Your Microsoft Windows Lab Workspace (3:50)</li><li>Lab: Final Project Part 1: Windows</li><li>Lab: Final Project Part 2: Linux Tasks</li><li>Peer Review</li></ul><div>Course Wrap-Up</div><ul><li>Video: Course Wrap-Up (7:22)</li><li>Congratulations and Next Steps</li><li>Thanks from the Team</li><li>Copyrights and Trademarks</li></ul><div>Course Rating<br /><br />Digital Badge</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>
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Operating Systems and Security Fundamentals

This course covers the core concepts common to all operating systems, with a focus on the unique features of Windows, Linux, and macOS.

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  <div>Cyberattacks have been on the rise and are expected to continue growing, highlighting the increasing need for skilled cybersecurity professionals. Start your journey into the world of cybersecurity with this introductory IBM course, designed to introduce you to key concepts, common threats, and effective preventive measures. <br /><br />Dive into the evolution of cybersecurity, explore the motivations behind cyberattacks, and learn about various types of threats such as malware and ransomware. The course will also help you apply critical thinking to common cybersecurity practices and architectural frameworks. <br /><br /></div><div>Gain valuable insights from industry experts who share their career journeys, skills, and perspectives. You'll also explore the role of cybersecurity frameworks, standards, and organizations in protecting against attacks. <br /><br /></div><div>Throughout the course, you’ll build hands-on technical skills and practical knowledge through interactive labs. The final project will allow you to showcase your understanding of key cybersecurity principles. <br /><br /></div><div>This course is perfect for anyone interested in learning the basics of cybersecurity and will help you launch a career as a Cybersecurity Analyst.<br /><br /></div><h1>Learning Outcomes</h1><ul><li>Start your career in cybersecurity with this course, covering essential foundational concepts. </li><li> Learn how to apply key cybersecurity principles, tools, and strategies to safeguard both digital and physical assets. </li><li> Identify common security threats and best practices to protect against them. </li><li> Understand authentication and identity management techniques to secure your organization. </li></ul><h1>Course Syllabus</h1><div>Welcome</div><ul><li>Video: Course Introduction</li><li>General Information</li><li>Learning Objectives and Syllabus</li><li>Grading Scheme</li><li>Helpful Tips for Course Completion</li></ul><div>Module 1: Cybersecurity Insights</div><ul><li>Learning Objectives and Module Introduction</li><li>Video: The Impact of the WarGames Movie on Cybersecurity</li><li>Video: The Impact of 9/11 on Cybersecurity</li><li>Video: The Impact of Covid 19 on Cybersecurity</li><li>Reading: The History of Cybersecurity</li><li>Practice Quiz</li><li>Video: Critical thinking in Cybersecurity</li><li>Video: Critical Thinking: A Model</li><li>Video: Critical Thinking: 5 key Skills</li><li>Activity: Would You Rather?</li><li>Practice Quiz</li><li>Module 1 Summary: Cybersecurity Insights</li><li>Graded Quiz: Cybersecurity Insights</li><li>Module 1 Glossary: Cybersecurity Insights</li></ul><div>Module 2: Cybersecurity Threats</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Types of Actors and Their Motives</li><li>Reading: Insider Attacks</li><li>Video: Cybersecurity Trends</li><li>Activity: Exploring IBM's X-Force Threat Intelligence Index</li><li>Practice Quiz</li><li>Video: What is Malware? Let's Hear the Hacker's Viewpoint</li><li>Video: What is Ransomware?</li><li>Reading: Malware Types</li><li>Video: How to Prevent Malware Attacks</li><li>Reading: Malware Detection with Rootkit Hunters</li><li>Hands-on Lab: Malware Scan using Rkhunter</li><li>Practice Quiz</li><li>Video: Social Engineering - How Bad Guys Hack Users</li><li>Reading: Types of social engineering</li><li>Video: What is Phishing?</li><li>Video: Humans vs. AI Who's Better at Phishing?</li><li>Reading: How to Prevent Social Engineering</li><li>Practice Quiz</li><li>Module 2 Summary: Cybersecurity Threats</li><li>Module 2 Graded Quiz: Cybersecurity Threats</li><li>Module 2 Glossary: Cybersecurity Threats</li></ul><div>Module 3: Cybersecurity Controls</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Internet Security Threats: Mapping</li><li>Video: Internet Security Threats: Packet Sniffing</li><li>Video: Internet Security Threats: IP Spoofing</li><li>Video: Internet Security Threats: Denial-of-Service Attacks</li><li>Video: Internet Security Threats: Injection Attacks</li><li>Practice Quiz</li><li>Video: Security Controls</li><li>Reading: Types of Controls</li><li>Video: System Security</li><li>Video: Network Security</li><li>Video: Application Security by Design</li><li>Video: Vulnerability Management</li><li>Video: Incident Response and Digital Forensics</li><li>Practice Quiz</li><li>Module 3 Summary: Cybersecurity Controls</li><li>Module 3 Graded Quiz: Cybersecurity Controls</li><li>Module 3 Glossary: Cybersecurity Controls</li></ul><div>Module 4: Identity and Physical Controls</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Identity and Access Management (IAM)</li><li>Video: Authentication</li><li>Video: Authorization</li><li>Video: Access Controls</li><li>Video: What is Multi-Factor Authentication?</li><li>Activity: Enable Two-Factor Authentication</li><li>Video: What is Single Sign On (SSO)?</li><li>Video: Passkeys FIDO</li><li>Practice Quiz</li><li>Video: Physical Threats and Controls</li><li>Reading: Physical Cybersecurity Controls</li><li>Practice Quiz</li><li>Module 4 Summary: Identity and Physical Controls</li><li>Module 4 Graded Quiz: Identity and Physical Controls</li><li>Module 4 Glossary: Identity and Physical Controls</li></ul><div>Module 5: Final Project - Secure Access</div><ul><li>Module Introduction and Learning Objectives</li><li>Reading: Final Project Overview</li><li>Final Project: Secure Access</li></ul><div>Course Wrap-Up</div><ul><li>Video: Course Wrap-up</li><li>Congratulations and Next Steps</li><li>Thanks from the Course Team</li><li>Copyrights and Trademarks</li></ul><div>Digital Badge</div><ul><li>Claim your Badge Here</li></ul><div>Course Rating and Feedback</div><ul><li>Course Rating and Feedback</li></ul><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>
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Cybersecurity Tools and Cyberattacks Fundamentals

Identify threats, implement security measures, and protect data. Jumpstart your cybersecurity career with this introductory course.

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  <div>Education is evolving, and AI-powered learning is transforming how students engage with knowledge. Some learners need detailed explanations, while others prefer quick, to-the-point answers. That’s where DeepSeek-R1 and Distill-Llama-70B step in, each offering a unique approach to AI-driven education.</div><div><br />DeepSeek-R1 is like a seasoned professor, providing comprehensive, well-structured explanations—ideal for students who need deep learning and thorough understanding. Distill-Llama-70B, in contrast, acts as an efficient tutor, delivering fast, accurate responses without compromising clarity, making it perfect for quick learning and interactive quizzes.</div><div><br />While R1 ensures a strong foundation through detailed insights, Distill-Llama-70B prioritizes speed and efficiency, catering to students who need rapid comprehension. Both models are shaping the future of AI-powered education, adapting to different learning styles and making learning more accessible, engaging, and effective.</div><div><br /></div><h1>A Look at the Project Ahead</h1><div>You will be able to do as a result of completing your project:</div><ul><li><strong>Learn DeepSeek’s different models</strong> for AI-powered education.</li><li><strong>Compare DeepSeek’s different models</strong> to find the best one for AI teaching.</li><li><strong>Generate personalized lesson plans</strong> using DeepSeek AI.</li><li><strong>Train AI to answer student questions</strong> with DeepSeek's advanced reasoning.</li></ul><div><br /></div><h1>What You'll Need</h1><div>Before starting this project, ensure you have:</div><ul><li><strong>Technical Requirements:</strong> A basic understanding of Python programming, including familiarity with libraries such as os for environment variable management.</li><li><strong>Browser Setup:</strong> A web browser for accessing tools and running code.</li></ul><div>By the end of this project, you'll be equipped with the knowledge and skills to effectively implement DeepSeek R1 in your applications, opening doors to innovative AI-driven solutions.</div>
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DeepSeek R1 Reasoning Model Compared for Educational Chatbot

In this guided project, you’ll explore different versions of DeepSeek-R1, large-parameter R1 and Distilled models, to create an AI-powered educational chatbot that enhances learning. Learn how to use DeepSeek-R1 to analyze student behavior, simplify complex concepts, and generate thought-provoking questions to personalize learning. Master DeepSeek’s advanced AI reasoning to provide real-time feedback and create adaptive learning experiences that truly engage users. Whether you're an educator or AI enthusiast, this project equips you with the skills to create smarter more effective AI solutions

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  <div>In today’s digital landscape, data is essential to the global economy, and cyberattacks often focus on stealing sensitive information or compromising systems. <br /><br />If you're interested in starting a career in cybersecurity or simply want to learn how it works, this five-module course on Cybersecurity Architecture is perfect for you. You’ll be guided by an experienced IBM cybersecurity engineer with extensive knowledge of the field. <br /><br /></div><div>This course covers the core fundamentals of cybersecurity, including the five security principles, the CIA triad, Identity and Access Management (IAM), endpoint security, and security domains. You’ll also dive into detection and response strategies, gaining a well-rounded understanding of prevention, detection, and response in cybersecurity. <br /><br /></div><div>Through practical activities, labs, and quizzes, you’ll apply what you’ve learned and test your knowledge at each step. Explore the world of cybersecurity and build a strong foundation for your future career! <br /><br /></div><h1>Learning Outcomes</h1><ul><li>Explain the five core principles of cybersecurity and the key concepts of the Confidentiality, Integrity, and Availability (CIA) triad </li><li>Outline the various areas a cybersecurity architect must address to design a secure enterprise solution. </li><li>Discuss key security domains, such as IAM, endpoint security, network security, application security, and data security </li><li>Show your ability to recommend strategies for enhancing network security in a specific scenario </li></ul><h1>Course Syllabus</h1><div>Welcome</div><ul><li>Video: Course Introduction (3:59)</li><li>General Information</li><li>Prerequisites and Course Syllabus</li><li>Grading Scheme</li><li>Helpful Tips for Course Completion</li></ul><div><br />Module 1: Cybersecurity Architecture Overview</div><ul><li>Module 1 Introduction and Learning Objectives</li><li>Video: Five Principles to Follow (and One to Avoid) (17:02)</li><li>Reading: Key Takeaways: Five Principles</li><li>Reading: How to Implement Least Privilege Security</li><li>Activity: Apply the Concept of Least Privilege</li><li>Video: Fundamentals of Confidentiality, Integrity, and Availability (12:25)</li><li>Reading: Key Takeaways: CIA Fundamentals</li><li>Reading: CIA Triad</li><li>Activity: Identify Compromised CIA Principles</li><li>Video: Roles and Tools (13:57)</li><li>Reading: Key Takeaways: Roles and Tools</li><li>Reading: NIST Cybersecurity Framework</li><li>Module 1 Summary: Cybersecurity Architecture Overview</li><li>Module 1 Glossary: Cybersecurity Architecture Overview</li><li>Module 1 Practice Quiz: Cybersecurity Architecture Overview</li><li>Module 1 Graded Quiz: Cybersecurity Architecture Overview</li></ul><div>Module 2 Access Management and Endpoint Security</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Who Are You? Identity and Access Management Really Wants to Know (31:04)</li><li>Reading: Key Takeaways: IAM</li><li>Reading: Understanding Multi-Factor Authentication</li><li>Activity: Apply Multi-Factor Authentication</li><li>Video: Endpoints Are the IT Front Door - Guard Them (13:51)</li><li>Reading: Key Takeaways: Endpoint Security</li><li>Reading: Endpoint Management with IBM</li><li>Activity: Implementing Endpoint Management</li><li>Module 2 Summary: Access Management and Endpoint Security</li><li>Module 2 Glossary: Access Management and Endpoint Security</li><li>Module 2 Practice Quiz: Access Management and Endpoint Security</li><li>Module 2 Graded Quiz: Access Management and Endpoint Security</li></ul><div>Module 3 Network, Application, and Data Security</div><ul><li>Module 3 Introduction and Learning Objectives</li><li>Video: Network Security (27:23)</li><li>Reading: Key Takeaways: Network Security</li><li>Reading: Securing Network Infrastructure</li><li>Video: Lab Demo (3:18)</li><li>Hands-on Lab: Secure Network Diagram</li><li>Video: Application Security (16:27)</li><li>Reading: Key Takeaways: Application Security</li><li>Reading: Secure Coding Practices</li><li>Video: Data Security (14:39)</li><li>Reading: Key Takeaways: Data Security</li><li>Reading: Data Security: Why a Proactive Stance is Best</li><li>Reading: Quantum Safe Encryption</li><li>Module 3 Summary: Network, Application, and Data Security</li><li>Module 3 Glossary: Network, Application, and Data Security</li><li>Module 3 Practice Quiz: Network, Application, and Data Security</li><li>Module 3 Graded Quiz: Network, Application, and Data Security</li></ul><div>Module 4 Detection and Response</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Detection (17:01)</li><li>Reading: Key Takeaways: Detection</li><li>Reading: Indicators of Compromise</li><li>Reading: Threat Intelligence</li><li>Hands-on Lab: Review Recent Security Threat Reports using X-Force Threat Exchange</li><li>Video: Response (15:49)</li><li>Reading: Key Takeaways: Response</li><li>Reading: Incident Response Frameworks</li><li>Activity: Incident Response</li><li>Reading: Case Study - Stopping Cyberattacks at a Major International Airport</li><li>Module 4 Summary: Detection and Response</li><li>Module 4 Glossary: Detection and Response</li><li>Module 4 Practice Quiz: Detection and Response</li><li>Module 4 Graded Quiz: Detection and Response</li></ul><div>Module 5 Final Project and Exam</div><ul><li>Module Introduction and Learning Objectives'</li><li>Reading: Final Project: Recommendations to Improve Network Security</li><li>Final Project Graded Quiz: Recommendations to Improve Network Security</li><li>Reading: Case Study: Network Architecture Powered by IBM</li><li>Course Glossary: Cyber Defense Architecture</li><li>Final Exam: Cyber Defense Architecture</li></ul><div>Course Wrap-Up</div><ul><li>Course Summary: Cyber Defense Architecture</li><li>Congratulations and Next Steps</li><li>Thanks from the Course Team</li><li>Copyrights and Trademarks</li></ul><div>Course Rating and Feedback<br /><br />Badge<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><h1>Recommended Skills Prior to Taking this Course</h1><div>Cybersecurity and Networking Fundamentals knowledge.  </div>
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Digital Security Architecture

Learn the fundamentals of cybersecurity architecture, including the CIA triad, IAM, endpoint security, and detection tools. 

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  <h1>About this course</h1><div>In this capstone project course you will apply your deep learning skills and demonstrate your mastery with deep neural networks.</div><div> </div><div>You’ll begin by loading data in PyTorch and Keras. You will explore the various methods and tools to efficiently handle datasets in both libraries, providing a foundation for model development.</div><div> </div><div>Next, the course covers data preprocessing. You’ll prepare images and other data for model training in both PyTorch and Keras. Through labs, you will gain experience in applying different preprocessing techniques such as scaling, normalization, and augmentation.</div><div> </div><div>The course then focuses on model training. You’ll apply the concept of pre-trained models and how to use them in both PyTorch and Keras. By participating in hands-on labs and peer reviews, you will develop a deeper understanding of model training, fine-tuning, and optimization.</div><div> </div><div>Once familiar with the training process, you will compare the performance of models built with PyTorch and Keras. In this section, you will analyze and evaluate the two frameworks, and assess the advantages and challenges of each through comparative exercises.</div><div> </div><div>Finally, the course concludes with a section dedicated to assessing the models you’ve trained. By using pre-trained models and comparing them to custom-built models, you’ll refine your evaluation techniques, gaining insight into the strengths and weaknesses of different model architectures.<br /><br /></div><h1>What you will learn: </h1><div>After completing this course, you will be able to: </div><ul><li>Demonstrate mastery in deep learning.</li><li>Describe the process of loading data into PyTorch and Keras, and the methods available for preprocessing data.</li><li>Apply data loading and preprocessing techniques in PyTorch and Keras to prepare datasets for machine learning tasks.</li><li>Analyze and compare the performance of models trained in PyTorch and Keras, evaluating the impact of different architectures and preprocessing methods.</li><li>Evaluate pre-trained models and assess their performance for real-world machine learning tasks.</li><li>Create workflows to train, compare, and assess models using PyTorch and Keras.</li></ul><div><br /></div><h1>Course Syllabus</h1><div><strong>Chapter 1 - Loading Data<br /></strong><br /></div><div><strong>Getting Started</strong></div><ul><li>Getting Started</li><li>Learning Objectives</li></ul><div><strong>1.1 Loading Data - PyTorch</strong></div><ul><li>Video: Loading Data - PyTorch</li><li>Lab: Loading Data - PyTorch</li><li>Quiz: Loading Data - PyTorch</li></ul><div><strong>1.2 Loading Data - Keras</strong></div><ul><li>Video: Loading Data -Keras</li><li>Lab: Loading Data - Keras</li><li>Quiz: Loading Data - Keras</li></ul><div><br /><strong>Chapter 2 - Data Preparation</strong></div><ul><li>Learning Objectives</li></ul><div><strong>2.1 Processing Data - PyTorch</strong></div><ul><li>Video: Image Preprocessing PyTorch</li><li>Lab: Processing Data - PyTorch</li><li>Quiz: Processing Data - PyTorch</li></ul><div><strong>2.2 Processing Data - Keras</strong></div><ul><li>Video: Processing Data - Keras</li><li>Lab: Processing Data - Keras</li><li>Quiz: Processing Data - Keras</li></ul><div><br /><strong>Chapter 3 - Assessment Training Model</strong></div><ul><li>Learning Objectives</li></ul><div><strong>3.1 Training Models - PyTorch</strong></div><ul><li>Video: Pre-trained Models PyTorch</li><li>Lab: Training Model - PyTorch</li><li>Peer Review: Training Models - PyTorch</li></ul><div><strong>3.2 Training Models - Keras</strong></div><ul><li>Video: Pre-trained Models in Keras</li><li>Lab: Training Models - Keras</li><li>Peer Review: Training Models - Keras</li></ul><div><br /><strong>Chapter 4 - Compare Two Models</strong></div><ul><li>Learning Objectives</li></ul><div><strong>4.1 Compare Two Models - PyTorch</strong></div><ul><li>Instruction: Compare Two Models - PyTorch</li><li>Lab: Compare Two Models - PyTorch</li><li>Quiz: Compare Two Models - PyTorch</li></ul><div><strong>4.2 Compare Two Models - Keras</strong></div><ul><li>Instruction: Compare Two Models - Keras</li><li>Lab: Compare Two Models - Keras</li><li>Quiz: Compare Two Models - Keras</li></ul><div><strong>Course Rating and Feedback</strong></div><ul><li>Course Rating and Feedback</li></ul><div><strong>Badge</strong></div><ul><li>Badges Frequently Asked Questions</li><li>Claim your badge here</li></ul><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>High School level Mathematics</li><li>Working knowledge of Python.</li><li>Machine Learning with Python</li><li>Fundamentals of Deep Learning</li><li>Working knowledge of PyTorch</li><li>Working knowledge of Keras/Tensorflow</li></ul>
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Deep Learning Capstone Project

Demonstrate your mastery in deep learning with PyTorch and Keras. Compare models, use pre-trained models, train new models, and optimize machine learning workflows with practical hands-on labs.

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  <h1>About this course</h1><div>In this course, you will begin by understanding the foundations of PyTorch for deep learning. You’ll explore how to calculate and implement logistic regression using PyTorch for binary classification tasks.</div><div> </div><div>Next, you’ll dive into SoftMax regression and how it’s used for multi-class classification. By learning to implement the SoftMax function in PyTorch, you will gain an understanding of how to apply SoftMax regression to multi-class problems and fine-tune models accordingly.</div><div> </div><div>The course then shifts to shallow neural networks, where you will explore how neural networks function with a single input layer and gradually progress to networks with multiple hidden layers. Through practical exercises, you will implement backpropagation and activation functions, which are crucial for effective learning in neural networks.</div><div> </div><div>Deep neural networks are introduced, and you’ll learn to design more complex architectures. Techniques such as dropout for regularization, the role of weight initialization, and momentum in gradient descent will be covered. Additionally, batch normalization will be explored to stabilize and accelerate training.</div><div> </div><div>Next, you will study convolutional neural networks (CNNs), which are especially powerful for image classification. You will implement CNNs using PyTorch, learning to work with convolution layers, pooling layers, and multiple input-output channels. Furthermore, you’ll explore how to integrate GPUs for faster computation and take advantage of PyTorch’s torch vision models.</div><div> </div><div>The course culminates in a hands-on final project where you’ll apply the concepts you’ve learned by building a classification model for the Fashion MNIST dataset. This project will allow you to put all the skills into practice, from model development to fine-tuning and evaluation.</div><div><br />  </div><h1>What you will learn: </h1><div>After completing this course, you will be able to: </div><ul><li>Explain the concept of logistic regression, cross-entropy loss, and the SoftMax function.</li><li>Apply PyTorch for implementing logistic regression, SoftMax regression, and neural network models.</li><li>Analyze the role of activation functions, backpropagation, and regularization techniques like dropout in deep neural networks.</li><li>Evaluate the effectiveness of deep learning architectures, including shallow and convolutional neural networks, in various machine learning tasks.</li><li>Create and optimize complex neural networks for classification tasks, utilizing techniques such as batch normalization and gradient descent with momentum.</li></ul><div><br /></div><h1>Course Syllabus</h1><div><strong>Module 1: Logistic Regression Cross Entropy Loss  <br /></strong><br /></div><div><strong>Welcome</strong></div><ul><li>Video: Course Introduction</li><li>Reading: Course Introduction</li><li>Reading: Course Overview</li><li>Helpful Tips for Course Completion</li><li>Learning Objectives and Syllabus</li><li>Grading Scheme</li></ul><div><strong>Logistic Regression Cross Entropy Loss  </strong></div><ul><li>Logistic Regression Cross Entropy Loss</li><li>Logistic Regression Mean Square Error</li><li>Logistic Regression Cross Entropy  </li><li>Module-level Graded Quiz: Logistic Regression Cross Entropy Loss </li></ul><div><br /><strong>Module 2: Softmax Regression </strong></div><div><br /><strong>Softmax Prediction </strong></div><ul><li>Softmax</li></ul><div><strong>Softmax function</strong></div><ul><li>Softmax function: Using Lines to classify data Prediction</li></ul><div><strong>Softmax PyTorch </strong></div><ul><li>Softmax PyTorch</li><li>Softmax Classifier 1</li><li>Softmax Classifier 2</li><li>Module-level Graded Quiz: Softmax Regression   </li></ul><div><br /><strong>Module 3: Shallow Neural Networks   <br /></strong><br /></div><div><strong>Neural Networks in One Dimension </strong></div><ul><li>What’s a neural network?</li><li>Neural Networks in One Dimension</li><li>Graded Quiz:Neural Networks </li></ul><div><strong>Neural Networks More Hidden Neurons </strong></div><ul><li>More Hidden Neurons </li><li>More Hidden Neurons </li></ul><div><strong>Neural Networks with Multiple Dimensional Input</strong></div><ul><li>Neural Networks with Multiple Dimensional Input</li><li>Multi-Dimensional Neural Networks  </li></ul><div><strong>Multi-Class Neural Networks </strong></div><ul><li>Multi-Class Neural Networks</li><li>Multi-Class Neural Networks with MNIST</li></ul><div><strong>Backpropagation</strong></div><ul><li>Backpropagation</li></ul><div><strong>Activation Functions </strong></div><ul><li>Activation Functions </li><li>Activation Functions </li><li>Neural network with different Activation Functions </li><li>Module-level Graded Quiz:Multi-Class Neural Networks </li></ul><div><br /><strong>Module 4: Deep Networks  <br /></strong><br /></div><div><strong>Deep Neural Networks</strong></div><ul><li>Deep Neural Networks</li><li>Deep Neural Networks</li><li>Graded Quiz: Deep Neural Networks</li><li>Deeper Neural Networks: nn.ModuleList()</li><li>Deeper Neural Networks : nn.ModuleList()</li></ul><div><strong>Dropout</strong></div><ul><li>Dropout</li><li>Dropout Classification</li><li>Dropout Regression</li></ul><div><strong>Neural Network Initialization Weights </strong></div><ul><li>The Role of Dropout in Regularization and Model Generalization</li><li>Neural Network Initialization Weights  </li><li>Initialization with Same Weights</li><li>Initialization Xavier</li><li>Understanding the Importance of Initialization Techniques in Deep Learning</li></ul><div><strong>Gradient Descent with Momentum </strong> </div><ul><li>Gradient Descent with Momentum</li><li>Momentum with Different Polynomials </li><li>Neural networks with momentum </li></ul><div><strong>Batch Normalization</strong></div><ul><li>Batch Normalization   </li><li>Batch Normalization with the MNIST Dataset</li><li>Graded Quiz: Batch Normalization   </li><li>Module-level Graded Quiz:Deep Networks</li></ul><div><br /><strong>Module 5: Convolutional Neural Networks <br /></strong><br /></div><div><strong>Convolution</strong></div><ul><li>Convolution </li><li>What's Convolution </li></ul><div><strong>Activation Functions and Max Pooling  </strong> </div><ul><li>Activation Functions and Max Pooling  </li><li>Activation Functions and Max Pooling  </li></ul><div><strong>Multiple Input and Output Channels</strong>   </div><ul><li>Multiple Input and Output Channels</li><li>Multiple Input and Output Channels</li></ul><div><strong>Convolutional Neural Network </strong></div><ul><li>Convolutional Neural Network </li><li>Convolutional Neural Network for MNIST  </li><li>Convolutional Neural Network Simple example </li><li>Convolutional Neural Network MNIST </li><li>Convolutional Neural Networks with Batch Norm (Honors only) </li></ul><div><strong>Torch Vision Models </strong></div><ul><li>Torch Vision Models </li></ul><div><strong>Graphics Processing Unit  </strong> </div><ul><li>Graphics Processing Unit</li><li>GPU Setup</li><li>Module-level Graded Quiz: Convolutional Neural Networks</li></ul><div><br /><strong>Module 6: Final Project <br /></strong><br /></div><div><strong>Final Project</strong></div><ul><li>Practice Project</li><li>Final Project</li><li>Fashion MNIST Classification Assignment</li></ul><div><strong>Course Wrap-up </strong></div><ul><li>Congratulations and Next Steps </li><li>Thanks from the Course Team </li></ul><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>High School level Mathematics</li><li>Working knowledge of Python.</li><li>Machine Learning with Python</li><li>Fundamentals of Deep Learning</li><li>Working knowledge of PyTorch</li></ul>
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PyTorch for Deep Learning

Learn key concepts in deep learning, from logistic and softmax regression to shallow & deep neural networks. Implement these models in PyTorch, mastering techniques like backpropagation, dropout, and convolutional networks for machine learning tasks.

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  <h1>About this course</h1><div>In this intermediate course, you’ll learn the essentials of PyTorch for machine learning techniques. Starting with tensors, you’ll understand their role in machine learning and how to perform basic operations with them. You’ll also explore differentiation in PyTorch, which is key for optimizing models. With hands-on labs, you’ll apply these concepts to work with simple and Torch Vision datasets, gaining experience in handling data for model training.</div><div> </div><div>You’ll then move into regression techniques, beginning with linear regression, where you’ll learn how to predict outcomes and optimize models using gradient descent. You’ll understand key concepts like loss functions and the steps involved in model training, which will prepare you for more complex machine learning tasks.</div><div> </div><div>The course will also cover more advanced regression techniques, including multiple input-output linear regression. You’ll learn how to handle multi-dimensional data, make predictions with multiple features and outputs, and train more sophisticated models.</div><div> </div><div>Classification is another key area of focus, where you’ll dive into logistic regression and learn how to apply it to solve classification problems. Through exercises, you’ll explore how to use linear classifiers, apply loss functions like Cross Entropy Loss, and implement effective models for binary classification.</div><div> </div><div>Finally, you’ll apply your learning to real-world problems. The practice project will involve building a neural network to classify breast cancer data, while the final project will challenge you to develop a League of Legends match predictor. You’ll work through data preparation, model building, and optimization, while receiving peer feedback to refine your skills.</div><div> </div><div>By the end of the course, you’ll be equipped to build and optimize machine learning models with PyTorch, solving regression and classification problems efficiently and effectively. You’ll gain the experience necessary to tackle machine learning challenges and develop your skills further in this rapidly growing field.<br /><br /></div><h1>What you will learn: </h1><div>After completing this course, you will be able to: </div><ul><li>Define tensors and understand their applications in PyTorch.</li><li>Explain linear regression and how to apply it using PyTorch.</li><li>Implement stochastic and mini-batch gradient descent algorithms.</li><li>Compare different methods of regression (linear, multiple) in terms of predictions and loss functions.</li><li>Assess the performance of a model using training, validation, and test splits.</li><li>Develop a final project that applies learned techniques to classify or predict outcomes in a dataset.</li></ul><div><br /></div><h1>Course Syllabus</h1><div><strong>Module 1: Tensors 1D<br /></strong><br /></div><div><strong>Welcome</strong></div><ul><li>Video: Course Introduction</li><li>Reading: Course Overview</li><li>General Information</li><li>Learning Objectives and Syllabus</li><li>Grading Scheme</li><li>Helpful Tips for Course Completion</li><li>Video: Overview of Tensors </li></ul><div><strong>Tensors 1D</strong></div><ul><li>Errata:Tensors ID</li><li>Video: Tensors 1D </li><li>Reading: Tensors 1D </li><li>Lab2: Tensors 1D </li></ul><div><strong>Two-Dimensional Tensors</strong></div><ul><li>Errata:Two-Dimensional Tensors </li><li>Video: Two-Dimensional Tensors </li><li>Lab: Two-Dimensional Tensors </li></ul><div><strong>Derivatives in PyTorch</strong></div><ul><li>Video: Differentiation in PyTorch </li><li>Lab: Differentiation in PyTorch </li></ul><div><strong>Simple Dataset</strong></div><ul><li>Video: Simple Dataset </li><li>Lab: Simple Dataset</li></ul><div><strong>Dataset</strong></div><ul><li>Video: Dataset </li><li>Lab: Dataset </li><li>Lab: Torch Vision Datasets </li><li>Module-level Graded Quiz: Tensor and Dataset</li></ul><div><br /><strong>Module 2: Linear Regression<br /></strong><br /></div><div><strong>Linear Regression Prediction</strong></div><ul><li>Video: Linear Regression Prediction </li><li>Lab: Linear Regression 1D: Prediction  </li></ul><div><strong>Linear Regression Training</strong></div><ul><li>Errata: Linear Regression Training  </li><li>Video: Linear Regression Training  </li><li>Video: Loss  </li></ul><div><strong>Gradient Descent and Cost</strong></div><ul><li>Video: Gradient Descent and Cost </li><li>Video: Cost  </li></ul><div><strong>PyTorch Slope</strong></div><ul><li>Video: Linear Regression PyTorch </li><li>Lab: Linear Regression ID: Training one Parameter</li></ul><div><strong>Linear Regression Training</strong></div><ul><li>Errata: PyTorch Linear Regression Training Slope and Bias  </li><li>Video: PyTorch Linear Regression Training Slope and Bias  </li><li>Lab: Linear Regression: Prediction </li><li>Module-level Graded Quiz: Linear Regression</li></ul><div><br /><strong>Module 3: Linear Regression PyTorch Way<br /></strong><br /></div><div><strong>Stochastic Gradient Descent and Data Loader</strong></div><ul><li>Video: Stochastic Gradient Descent  </li><li>Lab: Stochastic Gradient Descent and Data Loader   </li></ul><div><strong>Mini-Batch Gradient Descent</strong></div><ul><li>Video: Mini-Batch Gradient Descent  </li><li>Lab: Mini-Batch Gradient Descent  </li></ul><div><strong>Optimization in PyTorch</strong></div><ul><li>Video: Optimization in PyTorch </li><li>Lab: Optimization in PyTorch </li></ul><div><strong>Training, Validation, and Test Split</strong></div><ul><li>Video: Training, Validation, and Test Split </li><li>Errata: Training, Validation, and Test Split PyTorch</li><li>Video: Training, Validation, and Test Split PyTorch </li><li>Lab: Training, Validation, and Test Split in PyTorch </li><li>Module-level Graded Quiz: Linear Regression PyTorch Way</li></ul><div><br /><strong>Module 4: Multiple Input Output Linear Regression<br /></strong><br /></div><div><strong>Multiple Linear Regression Prediction</strong></div><ul><li>Video: Multiple Linear Regression Prediction </li><li>Lab: Multiple Linear Regression Prediction </li><li>Video: Multiple Linear Regression Training </li><li>Lab: Multiple Linear Regression Training </li></ul><div><strong>Multiple Output Linear Regression</strong></div><ul><li>Video: Linear Regression Multiple Outputs  </li><li>Lab: Multiple Linear Regression Prediction </li><li>Video: Multiple Output Linear Regression Training </li><li>Lab: Multiple Linear Regression Training </li><li>Module-level Graded Quiz: Multiple Input Output Linear Regression </li></ul><div><br /><strong>Module 5: Logistic Regression for Classification<br /></strong><br /></div><div><strong>Linear Classier</strong></div><ul><li>Video: Linear Classifiers  </li></ul><div><strong>Logistic Regressioin Prediction</strong></div><ul><li>Video: Logistic Regression Prediction   </li><li>Lab: Logistic Regression Prediction  </li></ul><div><strong>Bernoulli Distribution and Maximum Likelihood Estimation</strong></div><ul><li>Video: Bernoulli Distribution and Maximum Likelihood Estimation </li></ul><div><strong>Logistic Regression Cross Entropy Loss</strong></div><ul><li>Video: Logistic Regression Cross Entropy Loss    </li><li>Lab: Logistic Regression Mean Square Error </li><li>Lab: Logistic Regression Cross Entropy  </li><li>Module-level Graded Quiz: Linear Classifiers</li></ul><div><br /><strong>Module 6: Practice Project and Final Project<br /></strong><br /></div><div><strong>Practice Project and Final Project</strong></div><ul><li>Practice Project: Neural Network for Breast Cancer Classification</li><li>Reading: Final Project Overview</li><li>Final Project: League of Legends Match Predictor</li><li>Reading Peer-Review: Final Project League of Legends Match Predictor</li></ul><div><strong>Course Wrap-Up</strong></div><ul><li>Congratulations and Next Steps</li><li>Thanks from the Course Team</li><li>Reading: Copyrights and Trademarks</li></ul><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><h1>Recommended Skills Prior to Taking this Course</h1><div>The following skills are required to be successful with this course: </div><ul><li>High School level Mathematics</li><li>Working knowledge of Python.</li><li>Machine Learning with Python</li><li>Fundamentals of Deep Learning</li></ul>
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PyTorch Fundamentals for Machine Learning

Learn the fundamentals of PyTorch for machine learning in this course. Topics include tensors, linear regression, logistic regression, and optimization techniques like gradient descent. Apply your skills through hands-on projects and quizzes.

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  <h1>About this course</h1><div>In this Intermediate course, you will gain an in-depth understanding of Keras and its functionalities. Learn how to use the Keras Functional API to design flexible and complex models, and explore subclassing to create custom layers and models. You will become proficient in building custom deep learning architectures, utilizing TensorFlow 2.x to enhance model performance.</div><div> </div><div>You will dive into advanced techniques in convolutional neural networks (CNNs) and data augmentation. Learn how to augment data effectively to improve model accuracy and performance, and apply transfer learning using pre-trained models for image classification. You’ll also explore the use of transpose convolutions in image processing tasks.</div><div> </div><div>The course also covers unsupervised learning techniques, such as autoencoders and diffusion models, allowing you to create and train generative models like GANs. You’ll discover how to use these methods to generate new data and work with complex unsupervised tasks, laying the foundation for powerful machine learning applications.</div><div> </div><div>You will further enhance your skills by mastering advanced Keras techniques, such as custom training loops, hyperparameter tuning, and model optimization. Through hands-on labs, you’ll learn how to fine-tune models and optimize them for better accuracy and efficiency, equipping you with the tools to improve any deep learning model.</div><div> </div><div>The course also introduces reinforcement learning, where you will learn the principles behind Q-learning and deep Q-networks (DQNs). Gain practical experience in building and implementing these models, exploring their potential in solving decision-making problems and real-world applications.</div><div> </div><div>Finally, you will apply your new knowledge by tackling a real-world deep learning project. Using transfer learning, you will classify waste products, gaining hands-on experience in practical model deployment and problem-solving. By the end of the course, you will have a strong grasp of the latest advancements in deep learning and the skills needed to build and deploy cutting-edge models in Keras.<br /><br /></div><h1>What you will learn:</h1><div>After completing this course, you will be able to: </div><ul><li>Define advanced deep learning concepts, including Keras APIs, CNNs, transformers, GANs, and reinforcement learning principles.</li><li>Explain the inner workings of custom layers, data augmentation, transfer learning, and reinforcement learning algorithms.</li><li>Implement advanced techniques like custom training loops, model optimization, and generative models in Keras.</li><li>Compare and contrast CNNs, transformers, and unsupervised learning models, assessing their strengths for different use cases.</li><li>Optimize deep learning models through hyperparameter tuning and reinforcement learning strategies.</li><li>Design and develop advanced deep learning models using Keras, including transformers, GANs, and reinforcement learning models, and implement them in practical scenarios.</li></ul><div><br /></div><h1>Course Syllabus</h1><div><strong>Module 1: Advanced Keras Functionalities  <br /></strong><br /></div><div><strong>Welcome to the Course</strong></div><ul><li>Video: Course Introduction</li><li>Reading: Course Overview</li><li>Plugin/Reading: Helpful Tips for Course Completion</li></ul><div><strong>Advanced Keras Functional API </strong></div><ul><li>Video: Introduction to Advanced Keras </li><li>Video: Keras Functional API  and Subclassing API</li><li>Lab: Implementing the Functional API in Keras</li><li>Practice Quiz: Advanced Keras Functional API </li></ul><div><strong>Custom Layers with Keras</strong></div><ul><li>Video: Creating Custom Layers in Keras </li><li>Video: Overview of TensorFlow 2.x </li><li>Lab: Creating Custom Layers and Models </li><li>Practice Quiz: Custom Layers with Keras</li></ul><div><strong>Advanced Keras Functionalities  Summary</strong></div><ul><li>Reading: Summary and Highlights: Advanced Keras Functionalities </li><li>Reading: Glossary: Advanced Keras Functionalities </li><li>Graded Quiz: Advanced Keras Functionalities  </li><li>Discussion Prompt: Meet and Greet [ ungraded]</li></ul><div><strong>Module 2: Advanced CNNs in Keras </strong></div><div><br /><strong>Advanced CNNs and Data Augmentation</strong></div><ul><li>Video: Advanced CNNs in Keras </li><li>Video: Data Augmentation Techniques </li><li>Lab: Advanced Data Augmentation with Keras </li><li>Practice Quiz: Advanced CNNs and Data Augmentation</li></ul><div><strong>Transfer Learning on Pre-trained Models and Image Processing</strong></div><ul><li>Video: Transfer Learning in Keras </li><li>Video: Using Pre-trained Models </li><li>Lab: Transfer Learning Implementation </li><li>Video: TensorFlow for Image Processing </li><li>Practice Quiz: Transfer Learning on Pre-trained Models and Image Processing</li></ul><div><strong>Introducing Transpose Convolution</strong></div><ul><li>Video: Introducing Transpose Convolution </li><li>Lab: Practical Application of Transpose Convolution  </li><li>Practice Quiz: Introducing Transpose Convolution </li></ul><div><strong>Advanced CNNs in Keras Summary</strong></div><ul><li>Reading:  Summary and Highlights: Advanced CNNs in Keras</li><li>Reading: Glossary: Advanced CNNs in Keras</li><li>Graded Quiz: Advanced CNNs in Keras </li><li>Discussion Prompt: Data Augmentation and Transfer Learning</li></ul><div><strong>Module 3: Transformers in Keras  <br /></strong><br /></div><div><strong>Transformers in Keras  </strong></div><ul><li>Video: Introduction to Transformers in Keras </li><li>Video: Building Transformers for Sequential Data </li><li>Lab: Building Advanced Transformers   </li><li>Practice Quiz: Transformers in Keras </li></ul><div><strong>Advanced Transformers and Sequential Data using TensorFlow</strong></div><ul><li>Video: Advanced Transformer Applications </li><li>Video: Transformers for Time Series Prediction </li><li>Video: TensorFlow for Sequential Data </li><li>Lab: Implementing Transformers for Text Generation</li><li>Practice Quiz: Advanced Transformers and Sequential Data using TensorFlow </li></ul><div><strong>Transformers in Keras Summary</strong></div><ul><li>Reading: Summary and Highlight: Transformers in Keras  </li><li>Reading: Glossary: Transformers in Keras  </li><li>Graded Quiz: Transformers in Keras  </li><li>Discussion Prompt: Transforming Sequential Data with Transformers</li></ul><div><strong>Module 4: Unsupervised Learning and Generative Models in Keras <br /></strong><br /></div><div><strong>Unsupervised Learning, Autoencoders, and Diffusion Models </strong></div><ul><li>Video: Introduction to Unsupervised Learning in Keras </li><li>Video: Building Autoencoders in Keras </li><li>Lab: Building Autoencoders </li><li>Video: Diffusion Models </li><li>Lab: Implementing Diffusion Models </li><li>Practice Quiz: Unsupervised Learning, Autoencoders, and Diffusion Models </li></ul><div><strong>GANs and TensorFlow</strong></div><ul><li>Video: Generative Adversarial Networks (GANs) </li><li>Video: TensorFlow for Unsupervised Learning </li><li>Lab: Develop GANs using Keras </li><li>Practice Quiz: GANs and TensorFlow </li></ul><div><strong>Unsupervised Learning and Generative Models in Keras Summary</strong></div><ul><li>Reading: Summary and Highlight: Unsupervised Learning and Generative Models in Keras</li><li>Reading: Glossary: Unsupervised Learning and Generative Models in Keras</li><li>Graded Quiz: Unsupervised Learning and Generative Models in Keras </li><li>Discussion Prompt: Exploring Autoencoders and GANs</li></ul><div><strong>Module 5: Advanced Keras Techniques <br /></strong><br /></div><div><strong>Advanced Keras techniques and Custom Training Loops  </strong></div><ul><li>Video: Advanced Keras Techniques </li><li>Video: Custom Training Loops in Keras </li><li>Lab: Custom Training Loops in Keras </li><li>Practice Quiz: Advanced Keras techniques and Custom Training Loops </li></ul><div><strong>Hyperparameter and Model Optimization</strong></div><ul><li>Video: Hyperparameter Tuning with Keras Tuner </li><li>Lab: Hyperparameter Tuning with Keras Tuner </li><li>Video: Model Optimization </li><li>Video: TensorFlow for Model Optimization </li><li>Practice Quiz: Hyperparameter and Model Optimization </li></ul><div><strong>Advanced Keras Techniques Summary</strong></div><ul><li>Reading: Summary and Highlight: Advanced Keras Techniques </li><li>Reading: Glossary: Advanced Keras Techniques </li><li>Graded Quiz: Advanced Keras Techniques and Custom Training Loops</li><li>Discussion Prompt: Custom Training Loops and Hyperparameter Optimization</li></ul><div><strong>Module 6: Introduction to Reinforcement Learning with Keras <br /></strong><br /></div><div><strong>Reinforcement Learning, Q-Learning, Q-Networks (DQNs) </strong></div><ul><li>Video: Introduction to Reinforcement Learning</li><li>Video: Q-Learning with Keras </li><li>Lab: Implementing Q-Learning in Keras </li><li>Video: Deep Q-Networks (DQNs) with Keras </li><li>Lab: Building a Deep Q-Network with Keras</li><li>Practice Quiz: Reinforcement Learning, Q-Learning, Q-Networks (DQNs) </li></ul><div><strong>Module Summary</strong></div><ul><li>Reading: Summary and Highlight: Introduction to Reinforcement Learning with Keras</li><li>Reading: Glossary: Introduction to Reinforcement Learning with Keras</li><li>Graded Quiz: Introduction to Reinforcement Learning with Keras  </li><li>Discussion Prompt: The Promise and Challenge of Reinforcement Learning</li></ul><div><strong>Module 7: Final Project and Assignment</strong></div><ul><li>Reading: Practice Project Overview: Fruit Classification Using Transfer Learning</li><li>Lab:Practice Project: Fruit Classification Using Transfer Learning</li><li>Reading: Final Project: Classify Waste Products Using Transfer Learning</li><li>Final Project:  Classify Waste Products Using Transfer Learning</li><li>Project: Peer-graded Assignment:Classify Waste Products Using Transfer Learning</li></ul><div><strong>Course Wrap Up</strong></div><ul><li>Video: Course Wrap-up </li><li>Reading: Congratulations and Next Steps</li><li>Reading: Thanks from the Course Team</li></ul><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 in this course: </div><ul><li>Working knowledge of Python.</li><li>Machine Learning with Python</li><li>Fundamentals of Deep Learning with Keras</li></ul><h1><br /></h1>
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Keras and Tensorflow for Deep Learning

Enhance your deep learning skills with Keras and Tensorflow! Learn advanced CNN techniques, build transformers and GANs, dive into reinforcement learning, and optimize models. Apply your knowledge with hands-on labs and a final project using transfer learning.

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  <h1>About this course</h1><div>This course provides a comprehensive introduction to deep learning, covering key concepts, algorithms, and hands-on implementation using the Keras library. Through engaging lectures, practical labs, and quizzes, you’ll develop a solid foundation in deep learning techniques and gain experience building real-world models.</div><div> </div><div>Begin your deep learning journey with an introduction to the fundamental concepts of artificial intelligence, neural networks, and deep learning. Learn how artificial neurons mimic the human brain and explore the structure of neural networks. Through hands-on labs, you’ll gain practical experience in building artificial neural networks, reinforcing key principles before testing your knowledge with quizzes and assessments.</div><div> </div><div>Dive deeper into the mechanics of deep learning by understanding key optimization techniques such as gradient descent and backpropagation. Discover the challenges of training deep networks, including the vanishing gradient problem, and explore the role of activation functions in improving model performance. Practical labs provide hands-on experience in implementing these techniques, ensuring you grasp their real-world applications.</div><div> </div><div>Explore the power of Keras and other deep learning libraries used in AI development. Learn how to build regression and classification models using Keras, and apply your knowledge in lab exercises focused on implementing these models. This module equips you with essential tools for developing deep learning applications efficiently.</div><div> </div><div>Expand your deep learning expertise by exploring different model architectures. Compare shallow and deep neural networks, and gain insights into specialized models like convolutional neural networks (CNNs) for image processing, recurrent neural networks (RNNs) for sequence data, and transformers for advanced AI applications. Hands-on labs guide you through implementing CNNs and transformers in Keras, reinforcing theoretical concepts with practical applications.</div><div> </div><div>Put your knowledge to the test with a final project that challenges you to build a deep learning model for classification and captioning tasks. Apply the skills you’ve gained throughout the course to develop and evaluate your model, demonstrating your ability to create real-world AI solutions. Upon submission and evaluation, you’ll have a tangible project to showcase your deep learning expertise.</div><div> </div><div>This course is designed for professionals and students looking to enhance their deep learning skills. Whether you’re aiming for a career in AI, data science, or software development, this hands-on learning experience will provide the foundation you need.<br /><br /></div><h1>Learning Objectives: </h1><div>After completing this course, you will be able to: </div><ul><li>Define key concepts such as neural networks, deep learning models, and activation functions.</li><li>Explain the differences between supervised and unsupervised deep learning models.</li><li>Implement deep learning models using Keras to solve real-world problems.</li><li>Compare and contrast different deep learning architectures, including convolutional and recurrent neural networks.</li><li>Assess the performance of deep learning models using appropriate metrics.</li><li>Design, build, train, and test deep learning models using the Keras library.</li></ul><div><br /></div><h1><strong>Course Syllabus:</strong></h1><div>Welcome   </div><ul><li>Video: Course Introduction </li><li>General Information </li><li>Learning Objectives and Course Syllabus </li><li>Grading Scheme </li><li>Helpful Tips for Course Completion </li></ul><div>Introduction to Deep Learning and Neural Networks   </div><ul><li>Module Introduction and Learning Objectives </li><li>Video: Introduction to Deep Learning </li><li>Video: Neurons and Neural Networks  </li><li>Video: Artificial Neural Networks  </li><li>Lab: Artificial Neural Networks </li><li>Practice Quiz: Introduction to Deep Learning and Neural Networks </li><li>Reading: Summary and Highlights: Introduction to Deep Learning and Neural Networks </li><li>Graded Quiz: Introduction to Deep Learning and Neural Networks  </li></ul><div>Basics of Deep Learning   </div><ul><li>Module Introduction and Learning Objectives </li><li>Video: Gradient Descent  </li><li>Video: Backpropagation  </li><li>Lab: Backpropagation </li><li>Video: Vanishing Gradient  </li><li>Video: Activation Functions  </li><li>Lab: Vanishing Gradient and Activation Functions </li><li>Practice Quiz: Basics of Deep Learning </li><li>Reading: Summary and Highlights: Basics of Deep Learning </li><li>Graded Quiz: Basics of Deep Learning  </li></ul><div>Keras and Deep Learning Libraries   </div><ul><li>Module Introduction and Learning Objectives </li><li>Video: Deep Learning Libraries  </li><li>Video: Regression Models with Keras  </li><li>Lab: Regression with Keras </li><li>Video: Classification Models with Keras  </li><li>Lab: Classification with Keras </li><li>Practice Quiz: Keras and Deep Learning Libraries </li><li>Reading: Summary and Highlights: Keras and Deep Learning Libraries </li><li>Graded Quiz: Keras and Deep Learning Libraries </li></ul><div>Deep Learning Models   </div><ul><li>Module Introduction and Learning Objectives </li><li>Video: Shallow Versus Deep Neural Networks  </li><li>Video: Convolutional Neural Networks  </li><li>Lab: Convolutional Neural Networks with Keras </li><li>Video: Recurrent Neural Networks  </li><li>Video: Transformers  </li><li>Lab: Transformers with Keras </li><li>Video: Autoencoders  </li><li>Practice Quiz: Deep Learning Models </li><li>Reading: Summary and Highlights: Deep Learning Models </li><li>Graded Quiz: Deep Learning Models </li></ul><div>Final Project   </div><ul><li>Module Introduction and Learning Objectives </li><li>Final Project: Classification and Captioning</li><li>Final Project Submission and Evaluation</li></ul><div>Course Wrap-Up   </div><ul><li>Video: Course Wrap-Up  </li><li>Reading: Congratulations and Next Steps </li><li>Reading: Team and Acknowledgments </li><li>Copyrights and Trademarks </li></ul><div>Course Rating and Feedback   </div><ul><li>Course Rating and Feedback </li></ul><div>Badge   </div><ul><li>Claim Your Badge Here</li></ul><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><ul><li>Working knowledge of Python.</li><li>Machine Learning with Python</li></ul><div><br /><br /></div>
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Fundamentals of Deep Learning using Keras

Master the fundamentals of deep learning with IBM! This course covers neural networks, supervised & unsupervised models, and how to build, train, and test deep learning models using Keras. Gain hands-on skills to kickstart your AI career.

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  <div>Cybersecurity professionals are in high demand, and this trend is expected to continue for the foreseeable future, making now the perfect time to pursue a career in the field. <br /><br />This course offers an overview of the diverse career paths within cybersecurity. It is designed to equip you with the knowledge needed to find and secure the right role for you in the information security (Infosec) industry. The course begins by addressing the growing employment gap in cybersecurity and outlines the various roles and responsibilities at different career levels. <br /><br /></div><div>You will gain an understanding of both technical and soft skills, providing a well-rounded approach to professional growth. Additionally, the course will introduce you to some of the most respected certifications in the field, such as CompTIA A+ and ISC2’s CISSP, among others. <br /><br /></div><div>Throughout the course, you’ll hear from cybersecurity experts who will share valuable insights and personal experiences on how to break into the field. <br /><br /></div><div>This course is ideal for anyone looking to start a rewarding career in cybersecurity, regardless of prior experience or educational background. <br /><br /><br /></div><h1>Learning Outcomes</h1><ul><li>Compare cybersecurity career roles, responsibilities, and progression paths. </li><li>Align IT fundamentals, technical, and soft skills with cybersecurity job levels. </li><li>Identify key certifications (e.g., Security+, A+, CISSP, CEH) and their requirements. </li><li>Find the right cybersecurity role for you and learn how to prepare for it. </li></ul><h1>Course Syllabus</h1><div>Welcome</div><ul><li>Video: Course Introduction (5:47)</li><li>General Information</li><li>Learning Objectives and Syllabus</li><li>Grading Scheme</li><li>Helpful Tips for Course Completion</li></ul><div>Module 1: Cybersecurity Careers</div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Why Cybersecurity (3:15)</li><li>Reading: A Growing Field</li><li>Video: Expert Viewpoints: The Cybersecurity Field (5:54)</li><li>Video: How to get into the cybersecurity field (6:01)</li><li>Video: Entry Level Careers (3:22)</li><li>Reading: Entry-Level Roles, Responsibilities, and Salaries</li><li>Video: Expert Viewpoints: Getting started as a Cybersecurity Professional (5:33)</li><li>Video: Mid-Level Careers (3:41)</li><li>Reading: Mid-Level Roles, Responsibilities, and Salaries</li><li>Video: Expert Viewpoints: A Typical Day in the Life of a Cybersecurity Analyst (5:49)</li><li>Video: Advanced-Level Careers (4:44)</li><li>Reading: Advanced-Level Roles, Responsibilities, and Salaries</li><li>Lab: Cybersecurity Career Paths</li><li>Video: Expert Viewpoints: Cybersecurity Career Path (6:48)</li><li>Reading: Module 1 Summary : Cybersecurity Careers</li><li>Module 1 Practice Quiz : Cybersecurity Careers</li><li>Module 1 Glossary: Cybersecurity Careers</li><li>Module 1 Graded Quiz : Cybersecurity Careers</li><li>Discussion Prompt: Introduce yourself and why you are interested in Cybersecurity</li></ul><div>Module 2: Cybersecurity Skills</div><ul><li>Module Introduction</li><li>Video: Importance of IT Fundamentals Technical Skills (4:59)</li><li>Reading: IT Fundamentals Technical Skills Facts</li><li>Video: Expert Viewpoints: Fundamental IT Skills (4:20)</li><li>Video: Importance of Cybersecurity Technical Skills (4:22)</li><li>Reading: Cybersecurity Technical Skills Facts</li><li>Video: Expert Viewpoints:  Cybersecurity Technical Skills (4:02)</li><li>Video: Importance of Soft Skills (3:49)</li><li>Reading: Soft Skills Facts</li><li>Video: Expert Viewpoints: Soft Skills (5:55)</li><li>Reading: Skills to Job Role Mapping</li><li>Module 2 Summary: Cybersecurity Skills</li><li>Module 2 Practice Quiz: Cybersecurity Skills</li><li>Module 2 Glossary: Cybersecurity Skills</li><li>Module 2 Graded Quiz: Cybersecurity Skills</li></ul><div>Module 3: Cybersecurity Certifications</div><ul><li>Module Introduction</li><li>Video: Benefits of Cybersecurity Certifications (4:33)</li><li>Reading: Cybersecurity Certifications Facts</li><li>Video: Expert Viewpoints: Benefits of Industry Certifications (5:59)</li><li>Video: Overview of Cybersecurity Certifications (4:49)</li><li>Reading: CompTIA Certifications</li><li>Reading: ISC2 Certifications</li><li>Reading: Other Certifications</li><li>Video: Expert Viewpoints: Impact of Certifications (4:40)</li><li>Video: Choosing the Right Certification (2:50)</li><li>Video: Study for the Exam (3:18)</li><li>Video: Schedule and Take the Exam (3:16)</li><li>Video: Expert Viewpoints: Tips for Taking and Passing a Certification Exam (4:31)</li><li>Module 3 Summary: Cybersecurity Certifications</li><li>Module 3 Practice Quiz: Cybersecurity Certifications</li><li>Module 3 Glossary: Cybersecurity Certifications</li><li>Module 3 Graded Quiz: Cybersecurity Certifications</li></ul><div>Module 4: Final Project and Assessment</div><ul><li>Module Introduction and Learning Objectives</li><li>Final Project: Develop your Cybersecurity Career Plan</li><li>[Optional] Project Submission and Evaluation</li><li>Reading: Course Summary</li><li>Glossary (Cumulative)</li><li>Graded Quiz: Final Assessment (15 Questions)</li></ul><div>Course Wrap Up</div><ul><li>Video: Landing your first job in Cybersecurity (5:55)</li><li>Video: Expert Viewpoints: Tips for a Cybersecurity Career (5:02)</li><li>Video: Cybersecurity Career FAQ</li><li>Congratulations and Next Steps</li><li>Thanks from the Course Team</li><li>Copyrights and Trademarks</li></ul><div>Course Rating</div><ul><li>Course Rating and Feedback</li></ul><div>Badge</div><ul><li>Claim your Badge</li></ul><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><h1>Recommended Skills Prior to Taking this Course</h1><div>No prior experience is required. </div>
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Cybersecurity: Kickstart your Career

Launch your cybersecurity career with this course! Learn about roles, certifications, and key skills to succeed in the growing information security industry.

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  <div><br />Understanding how <strong>machine learning models</strong> make predictions is a critical skill, especially in high-stakes domains like <strong>healthcare</strong>. In this hands-on guided project, you’ll predict <strong>heart attack risks</strong> using <strong>Python</strong>, <strong>scikit-learn</strong>, and <strong>Explainable AI (XAI)</strong> techniques like <strong>Partial Dependence Plots (PDPs)</strong>. By working with a real-world <strong>heart disease dataset</strong>, you’ll not only create predictive models but also learn to interpret their outputs, uncovering how features such as age, cholesterol levels, and blood pressure influence predictions. Beyond technical skills, this project emphasizes using data to make informed decisions, empowering you to communicate findings that can positively impact patient care. In just 60 minutes, you'll gain a practical understanding of PDPs and their role in explaining machine learning models, equipping you to navigate the intersection of AI and healthcare.<br /><br /><strong>What You'll Learn</strong><br />By the end of this project, you will have mastered:</div><ul><li>- <strong>Interpreting machine learning models</strong> using <strong>Partial Dependence Plots (PDPs)</strong> to visualize feature impact.</li><li>- Understanding and analyzing <strong>feature interactions</strong> within models.</li><li>- Applying and comparing <strong>logistic regression</strong> and <strong>random forest models</strong> on healthcare data.</li><li>- Using metrics and visualization tools to evaluate model performance and interpretability effectively.</li></ul><div><br /><br /><strong>What You'll Need</strong><br />To get started with this guided project, you should have:</div><ul><li>- A basic understanding of Python programming.</li><li>- Familiarity with fundamental machine learning concepts.</li><li>- Access to modern web browsers like Chrome, Edge, Firefox, Internet Explorer, or Safari.</li></ul><div><br /><strong>Ready to demystify machine learning models?</strong> Start this guided project now and unlock the ability to interpret complex algorithms, transforming raw data into actionable healthcare insights.</div>
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Med Prediction with Explainable AI: Partial Dependence Plot

 Learn to interpret machine learning models by visualizing feature impacts using Python, scikit-learn, and Explainable AI (XAI) techniques like Partial Dependence Plots (PDPs). These visualizations show relationships between features like age, income, or medical metrics and model predictions, translating complex algorithms into clear insights. Data scientists and stakeholders can use PDPs to understand model behaviour. This interpretability technique supports technical analysis and business decision-making. 

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  <div>In an era where <strong>customer expectations are higher than ever</strong>, businesses must find innovative ways to <strong>deliver fast, reliable, and personalized support</strong>. This course introduces you to <strong>watsonx Assistant</strong>, a cutting-edge AI-powered virtual assistant that leverages <strong>Generative AI and Large Language Models (LLMs)</strong> to enhance customer and employee interactions while <strong>reducing operational costs</strong>.</div><div>Designed for beginners and professionals alike, this course explores <strong>the evolving role of AI assistants</strong> in modern customer service and workforce efficiency. You will learn how watsonx Assistant can improve user experiences, streamline operations, and free up valuable developer time for higher-impact projects.<br /><br /></div><div>By the end of this course, you will:</div><ul><li>Understand the limitations of traditional customer service and how human-led models can sometimes hinder brand perception.</li><li>Explore common pitfalls of existing chat technologies and why many are discarded due to poor performance.</li><li>Learn how watsonx Assistant enhances customer and employee experiences while delivering significant cost savings.</li><li>Discover how Generative AI is reshaping AI assistants and how LLM-powered features improve AI-driven interactions.</li><li>Shift from customer support to customer care, creating more engaging and effective interactions.</li><li>See how AI assistants optimize developer resources, allowing companies to focus on high-value innovation.</li><li>Identify the three key success factors in Generative AI deployment: trust, speed, and cost.</li><li>Explore the extensible architecture of watsonx Assistant, enabling seamless integration across platforms.</li></ul><div><br /></div><div>With a blend of <strong>theory, real-world applications, and hands-on insights</strong>, this course empowers you to <strong>harness the potential of watsonx Assistant</strong> for business success. Whether you're looking to improve customer support, automate workflows, or explore the impact of <strong>AI-powered virtual assistants</strong>, this course provides the foundation you need.</div>
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watsonx Assistant: Virtual Assistants with Generative AI

This introductory course on watsonx Assistant is designed for anyone interested in exploring the latest advancements in Generative AI and how they can enhance customer and employee experiences through the creation of AI assistants.

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  <div>In today’s fast-evolving digital landscape, AI-powered automation is revolutionizing the way businesses operate. This course provides a deep dive into <strong>watsonx Orchestrate</strong> and its role in <strong>Agentic AI</strong>, equipping you with the knowledge and skills to enhance business processes using intelligent automation.</div><div>Throughout the course, you will explore the power of <strong>AI assistants with agents</strong> and learn how to integrate them with business automation skills. You will gain hands-on experience in deploying intelligent assistants, leveraging automated decision-making, and implementing conversational search—all using <strong>watsonx Orchestrate’s low-code tools</strong>.</div><div><br /></div><div>By the end of this course, you will:</div><ul><li>Understand <strong>Agentic AI</strong> and its application in AI-driven business assistants.</li><li>Learn how to integrate <strong>business automation workflows</strong>—such as automated decision-making—into intelligent assistants.</li><li>Deploy a fully functional <strong>AI-powered assistant</strong> on your website with <strong>watsonx Orchestrate</strong>.</li><li>Explore <strong>conversational search</strong> and understand how to implement it for a more intuitive user experience.</li><li>Discover how <strong>LLMs (Large Language Models)</strong> facilitate seamless conversations between users and AI-driven services.</li><li>Utilize <strong>watsonx Orchestrate’s low-code environment</strong> to rapidly build and deploy <strong>Agentic AI</strong> solutions.</li></ul><div><br /></div><div>Whether you're a business professional, developer, or automation enthusiast, this course will equip you with the tools to <strong>streamline operations, enhance customer experiences, and drive efficiency</strong> with AI-powered automation.<br /><br /></div>
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watsonx Orchestrate: Automating Business with Agentic AI

During this course you will discover how to leverage your business automation capabilities in an agentic context. You will learn the power of AI assistants with agents and how to enrich them with business automation skills using watsonx Orchestrate.

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  <div>Imagine you’re working on a data science project, and you need AI assistance—whether it’s for <strong>data cleaning, feature engineering, or debugging your code</strong>. You turn to large language models (LLMs) like <strong>OpenAI o3-mini</strong> and <strong>DeepSeek R1</strong>, but how do you know which one is the best fit?<br /><br /></div><div>AI reasoning models are a <strong>hot topic</strong> in today’s world, with claims of superior problem-solving abilities and efficiency. But <strong>are they really all the same?</strong> Do some models <strong>think faster</strong>? Are others <strong>more accurate</strong> or <strong>cheaper to use</strong>? In this hands-on tutorial, we’ll <strong>put these models to the test</strong> across key stages of the <strong>data science workflow</strong> to help you decide which AI model can best support your work.<br /><br />With our <strong>AI Classroom</strong>, you can <strong>find out for yourself—quickly, easily, and at no cost</strong>. No credit card, no setup, no guesswork. Just log in, test models side by side, and make data-driven decisions <strong>in minutes</strong>.<br /><br /></div><h1><strong>Project Overview</strong></h1><div>This project is designed to <strong>compare the reasoning abilities</strong> of <strong>OpenAI o3-mini</strong> and <strong>DeepSeek R1</strong> in practical data science tasks. Instead of learning data science itself, you’ll focus on how well these AI models <strong>understand, process, and assist</strong> with different steps in a data workflow.<br /><br /></div><div>You'll evaluate both models based on:<br />✅ <strong>Speed</strong> – Which model generates responses faster?<br />✅ <strong>Cost</strong> – Which model is more cost-effective to use?<br />✅ <strong>Accuracy</strong> – Which model provides better and more correct answers?<br /><br /></div><div>Using the <strong>AI Classroom</strong>, you’ll conduct side-by-side comparisons of how these models perform in <strong>error correction, feature engineering, model selection, and more</strong>.<br /><br /></div><div>By the end, you'll have a <strong>data-driven perspective</strong> on which model is better suited for AI-assisted data science.<br /><br /></div><h1><strong> What You’ll Learn</strong></h1><div>By completing this project, you will:</div><ul><li>Understand <strong>how different reasoning models perform</strong> across real-world tasks.</li><li>Learn how <strong>AI can assist in data science workflows</strong> (but also its limitations).</li><li>Compare <strong>cost, accuracy, and efficiency</strong> of LLMs in a structured way.</li><li>Gain hands-on experience using the <strong>AI Classroom</strong> for model benchmarking.</li></ul><div><br /></div><h1><strong>Who Should Do This Project?</strong></h1><div>This project is for anyone curious about <strong>AI’s role in data science</strong> and wants to compare different reasoning models before integrating them into their workflow. It’s especially useful for:</div><ul><li><strong>Data scientists &amp; ML engineers</strong> evaluating LLMs for their work.</li><li><strong>AI enthusiasts &amp; researchers</strong> interested in model comparisons.</li><li><strong>Tech professionals &amp; developers</strong> looking to incorporate AI into data-driven projects.</li><li><strong>Students &amp; learners</strong> wanting to understand how AI reasoning models differ.</li></ul><div><br /></div><div>No extensive programming experience is needed, as this project focuses on <strong>prompt engineering and AI reasoning, rather than coding from scratch</strong>.<br /><br /></div><h1><strong> What You Need</strong></h1><div>Before starting, make sure you have:<br />✅ <strong>No setup required</strong> – Just open the AI Classroom and start testing!<br />✅ <strong>Basic knowledge of data science workflows</strong> – No need to be an expert, but familiarity helps!<br />✅ <strong>Curiosity &amp; willingness to experiment</strong> – The best way to learn is by testing and drawing your own conclusions!<br />✅ A <strong>current version of web browers: </strong>Chrome, Edge, Firefox, Internet Explorer, or Safari for the best experience.<br /><br /><br /></div><div>By the end of this project, you’ll have a clear <strong>understanding of how OpenAI o3-mini and DeepSeek R1 compare</strong>, allowing you to choose the best model for your AI-assisted data science tasks. </div>
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Compare DeepSeek-R1 vs OpenAI o3-mini on Data Science tasks

This project compares OpenAI o3-mini versus DeepSeek-R1 across key data science workflow tasks like feature engineering, model selection, and error correction—evaluating cost, speed, and accuracy. Find the best model for your needs in under 30 minutes! No credit card, no hassle—just hop into our AI Classroom and start running your own experiments instantly. Test, compare, and refine insights for free!

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  <h1><strong>How Many R’s Are Actually in Strawberry? AI Tries to Reason It Out</strong></h1><div>You’d think counting the number of <em>R’s</em> in “strawberry” is an easy task, right? Well, ask an AI, and you might get a surprising variety of answers—some correct, some overcomplicated, and some that make you question reality itself.</div><div>Modern AI reasoning models, like <strong>OpenAI’s o3-mini, DeepSeek’s R1, and IBM’s Granite-3.2-8B-Instruct-Preview</strong>, are designed to excel in logical inference, math, and real-time decision-making. But how well do they handle <strong>classic riddles and puzzles</strong>? Do they crack them effortlessly, or do they overthink themselves into oblivion?</div><div>In this guided project, we’ll put AI’s reasoning to the test—throwing tricky puzzles at different models to see which ones <em>get it</em> and which ones take a wild detour. Can AI solve riddles efficiently, or will it get tangled in its own logic like a confused detective overanalyzing a case? Let’s find out!  <br /><br /></div><h1><strong>A Look at the Project Ahead</strong></h1><div>Through this experiment, you’ll:<br />✅ <strong>Compare AI reasoning styles</strong> by testing multiple models on logic puzzles.<br />✅ <strong>Observe overthinking vs. direct solutions</strong>, identifying when excessive reasoning helps or hinders.<br />✅ <strong>Experiment with prompt engineering</strong> to guide AI reasoning effectively.<br />  
      <img src="https://author.skills.network/rails/active_storage/representations/redirect/eyJfcmFpbHMiOnsiZGF0YSI6NDE2NDQsInB1ciI6ImJsb2JfaWQifX0=--63378e6969b8e41b3a3bceea1e44a9e7420006f5/eyJfcmFpbHMiOnsiZGF0YSI6eyJmb3JtYXQiOiJwbmciLCJsb2FkZXIiOnsibiI6LTF9LCJyZXNpemVfdG9fbGltaXQiOlsxMDI0LDc2OF19LCJwdXIiOiJ2YXJpYXRpb24ifX0=--d5cef5cc4423e679c464df97b6ff7a8fa3dec7ab/reasoning-compare.png" />

  
      Comparing o3-mini and DeepSeek-R1-Distill_Llama-70B in Generative AI Classroom
  
</div><div>Some models might <strong>instantly recognize the trick.</strong> Others might <strong>overcomplicate the question</strong>, dissecting each word before arriving at an answer. By analyzing their approaches, you’ll learn how AI handles reasoning and where it still struggles.   <br /><br /></div><h1><strong>What You'll Need</strong></h1><div>Before starting, make sure you have:<br />🔹 <strong>Access to a Browser to run the Generative AI Classroom lab</strong>, where you can easily compare models side by side.<br />🔹 <strong>Basic understanding of logic puzzles</strong> (no programming required!).<br />🔹 <strong>Curiosity to explore AI’s strengths and quirks</strong> in solving problems.  <br /><br /></div><h1><strong>Key Takeaways: What You’ll Learn</strong></h1><div>🔍 <strong>How AI Thinks</strong> – Compare different models’ approaches to logic puzzles.<br />🧠 <strong>Overthinking vs. Clarity</strong> – Learn when detailed reasoning is beneficial and when it leads to unnecessary complexity.<br />📊 <strong>AI Strengths and Weaknesses</strong> – Discover where AI excels and where it struggles with common sense and probabilistic thinking.  <br /><br /></div><div>By the end of this project, you’ll have a hands-on understanding of <strong>how modern AI models reason, where they overcomplicate things, and how to refine their responses with better prompts.  <br /></strong><br /></div><h1><strong>Final Thought</strong></h1><div>AI reasoning isn’t just about <strong>getting the right answer</strong>—it’s about <strong>how</strong> the model arrives at it. While some models, like DeepSeek-R1, excel in step-by-step logic, they sometimes hesitate in drawing conclusions due to biases in their training. Others may simplify too much or get tangled in unnecessary details.</div><div>This project will help you appreciate <strong>the quirks of AI reasoning</strong> and recognize why tracking logical problem-solving is key to advancing AI’s real-world applications. So, let’s dive in, put these models to the test, and see which ones can outthink a brain teaser—and which ones need a reboot! 🚀</div>
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Overthinking AI? Comparing Top LLMs on Reasoning

OpenAI’s o3, DeepSeek-R1, and IBM’s Granite-3.2 are redefining problem-solving with logical thinking. This guided project puts their reasoning to the test with classic riddles, revealing their strengths, weaknesses, and tendencies to overthink. Compare responses from different models—who solves puzzles efficiently, and who gets lost in details? Observe how prompt instructions shape reasoning, from concise answers to step-by-step breakdowns. Gain insights into AI's thinking process and its balance between clarity and complexity.

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  <div><strong>Immerse Yourself in AI Innovation with DeepSeek's Cutting-Edge Framework<br /><br /></strong><br />This comprehensive guide unlocks the revolutionary AI ecosystem designed for developers, enterprise architects, and ML researchers seeking cost-efficient, open-source solutions. In 30 minutes, master DeepSeek's MoE-powered models that deliver GPT-4o-level performance at 1% operational cost. Learn to use the V3 model for real-time coding tasks and R1's advanced reasoning workflows. Whether optimizing enterprise NLP pipelines, building multilingual market analyzers, or deploying commercial AI products under MIT license, this project provides battle-tested blueprints for next-generation AI implementation.<br /><br /></div><div>You will use AI Classroom to complete this project. It is <strong>free of charge</strong>!<br /><strong><br />What You'll Master<br /><br /></strong>By completing this project, you will understand:</div><ul><li>Cost/performance comparisons between 7B-671B parameter models</li><li>V3's instant generation vs R1's complex decision-making patterns</li></ul><div><strong><br />What You'll Need<br /></strong><br /></div><div>Before starting, make sure you have:</div><ul><li>Access to the <strong>IBM Skills Network Labs environment</strong>, which includes tools like Docker.</li><li>A current version of <strong>Chrome, Edge, Firefox, Internet Explorer, or Safari</strong> for the best experience.</li></ul><div><strong><br /></strong><br /></div>
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DeepSeek Guide-Practical Tips You Don‘t Know

DeepSeek Guide: Which model should you choose, V3 or R1? Unleash revolutionary AI ecosystem through innovative MoE(Mixture of Experts) architecture and 90% training cost reduction. This 2025 handbook reveals strategies for choosing open-source models, commercial-ready solutions under MIT license, and comparisons of V3's instant task execution versus R1's advanced reasoning workflows. Master technical benchmarks, multilingual coding templates, and enterprise deployment blueprints for market analysis, content generation. 

<div>As Generative AI becomes critical to the remodelling of industries, it offers executives and business leaders a multitude of opportunities to drive innovation and business transformation. This IBM specialization empowers executives and business leaders to strategically leverage this technology and effectively integrate it into the organization. A series of short courses led by IBM thought leaders cover foundational knowledge of generative AI: its business applications and governance. You’ll learn to align AI strategies with organizational goals, work with compliance considerations, and explore cross-functional use cases in areas like customer service, HR, and finance. Using the IBM watsonx AI engine, you’ll become proficient in prompt-writing techniques and apply best practices to develop tailored genAI use cases for your business. By the end of the program, you’ll be equipped with actionable insights and a robust AI integration plan.<br><br>If you’re keen to collaborate with stakeholders and effectively integrate genAI in your enterprise, enroll today, and step into the future with confidence and clarity.<br><br>Applied Learning Project The final, practical, course of the program helps you formulate effective AI use cases for your business process.<br>Through a series of activities, you will apply genAI best practices while also using IBM’s watsonx AI engine to explore ways to use the tool in your organization. These hands-on activities will enable you to develop a potential use case by:</div><ul><li>&nbsp; Writing effective AI prompts and validating the output</li><li>Developing a business goal to address your challenges</li><li>Choosing an appropriate genAI application matching your goal</li><li>Critically examining use case feasibility by considering common risks and reflecting on ways to mitigate them</li><li>Throughout the courses, you will rely on your industry expertise and use that knowledge to finetune your ideas with assistance from AI</li></ul>

Generative AI for Executives and Business Leaders

Gain foundational knowledge of generative AI, its business applications, and its governance to strategically leverage and effectively integrate it into your organization.

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  <div>Imagine you’re an educator with a classroom full of students, each with their own unique learning journey. One student stands out—perhaps they excel in some subjects but struggle in others. You start to wonder: <strong>Are there students with similar backgrounds and study habits? What factors contribute to their success or challenges?</strong> Traditional AI models can predict outcomes, but they rarely explain <strong>why</strong> students succeed or struggle. This is where <strong>Explainable AI (XAI)</strong> comes in.<br /><br />In this hands-on project, you will explore IBM’s <strong>AI Explainability 360 (AIX360)</strong> toolkit to develop clear, actionable explanations for student outcomes. With the <strong>Protodash Explainer,</strong> you’ll identify representative student profiles and uncover key academic success factors. By leveraging <strong>transparent AI models</strong>, you’ll analyze relationships between <strong>study habits, demographics, and performance trends</strong>, making AI insights more interpretable.<br /><br />Understanding <strong>why</strong> a student is at risk is just as important as predicting their performance. This project highlights how explainability in AI empowers educators to <strong>design better learning strategies, create targeted interventions, and support students more effectively</strong>.<br /><br /></div><h1>A Look at the Project Ahead</h1><div>In this project, you’ll work with a real-world student dataset to explore factors influencing academic performance using transparent AI models. Learn how to preprocess data, apply interpretability techniques, and evaluate predictions using tools like the Protodash Explainer and PCA.</div><div>By the end of the project, you will:</div><ul><li><strong>Preprocess and encode datasets</strong> for AI-driven student performance analysis.</li><li><strong>Build and train interpretable models</strong> using Random Forest Classifiers and XAI tools like the Protodash Explainer.</li><li><strong>Identify similar student profiles</strong> using Protodash to understand key success factors and group dynamics.</li><li><strong>Visualize relationships</strong> between students and prototypes using <strong>PCA</strong> for dimensionality reduction.</li><li>Evaluate predictions and explanations to ensure the reliability and transparency of your models.</li></ul><div><br /></div><h1>What You'll Need</h1><div>To successfully complete this project, you’ll need:</div><ul><li>A foundational understanding of Python programming and libraries such as pandas, scikit-learn, and matplotlib.</li><li>Basic knowledge of AI and machine learning concepts, especially classification tasks.</li><li>A web browser to access tools and run your code.</li></ul><div><br />By the end of this project, you will have built an AI model that not only predicts student performance but also <strong>explains the reasoning behind each prediction</strong>, allowing educators to take meaningful action based on data-driven insights.</div>
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Learn Explainable AI by Analyzing Student Performance

Uncover hidden patterns in student success using Explainable AI (XAI) with IBM’s AI Explainability 360 (AIX360). You’ll step into the role of an educator trying to uncover why some students thrive while others struggle. Using Protodash Explainer, you’ll identify key student profiles and analyze patterns in study habits, demographics, and performance trends. Starting with data preprocessing, you’ll build AI models, apply PCA for visualization, and leverage Explainable AI (XAI) techniques to make AI-driven insights transparent and actionable. Perfect for data scientists and AI enthusiasts.

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  <div>As an executive or business leader, determining how best to integrate AI at the enterprise level will likely be your top priority. In this short course, IBM thought leaders share their views about integrating generative AI and unlocking its full potential. Listen to their guidance and gain insights into the key areas for your organization: </div><ul><li>Strategies </li><li>Elements of AI in enterprise </li><li>Putting AI to work </li></ul><div>In this short, self-paced course, these leaders share their views on harnessing Generative AI's potential, both cross-functionally and within select domains. You will receive practical guidance on adopting AI. No prior AI background is required. <br /><br /></div><h1>Course Learning Objectives</h1><ul><li>Aligning generative AI with business goals </li><li>Implementing AI elements enterprise-wide: models and data  </li><li>Transforming the enterprise using AI and data </li><li>Leveraging Generative AI in marketing, HR, ITOps, FinOps, and finance </li></ul><div><br /></div><h1>Course Syllabus</h1><div><strong>Module 1:</strong> <strong>Generative AI for Business Leaders</strong></div><ul><li>Video: From Pilot to Production: Driving ROI with GenAI </li><li>Video: Select the Right AI Use Case for your Business </li><li> Video: How Responsible AI Can Prepare You for Regulations </li><li>Video: Achieving AI-Readiness with Hybrid Cloud </li><li>Video: Is Data Management the Secret to Generative AI? </li><li>Video: Choose the Right AI Model for Your Use Case </li><li>Video: Putting AI to Work for Marketing </li><li>Video: Putting AI to Work for Talent </li><li>Video: Putting AI to Work for ITOps </li><li>Video: Putting AI to Work for FinOps </li><li>Video: Putting AI to Work in Finance</li><li>Reading: Course Summary: Putting AI to Work</li></ul><div><strong>Module 2:</strong> <strong>Final Quiz and Course Wrap Up</strong></div><ul><li>Graded Quiz: Final Quiz</li><li>Reading: [Optional] Video Companion Guidebooks</li><li>Reading: Congratulations and Next Steps</li><li>Reading: Thanks from the Course Team</li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>Leadership experience in an enterprise business or organization</div>
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GenAI for Execs & Business Leaders: Integration Strategy

In this short course, hear what IBM leaders think about the essentials when it comes to strategically integrating GenAI at scale cross-functionally and within their domains.  Here, you would find leaders share their views on GenAI's potential within their respective business segments.

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  <div>As an executive or business leader, effectively integrating Generative AI into your organization will likely be a top priority. However, using the tool to maximize its benefits can be challenging. This short course covers formulating genAI use cases for specific business processes. <br /><br />Through a series of hands-on activities, you will learn to apply genAI best practices while using IBM's watsonx AI engine. You will also learn how to find more efficient ways to leverage the tool in your organization and construct a potential use case. The course covers: </div><ul><li>Writing effective prompts </li><li>Developing a business goal to address challenges </li><li>Selecting an appropriate genAI application suited to the goal </li><li>Evaluating use case feasibility by considering common risks and reflecting on ways to mitigate them </li></ul><div>Throughout the course, you will use your knowledge and industry expertise to refine ideas for your organization with genAI assistance. <br /><br />To get the maximum value from this course, we recommend you take the first 2 courses in the Generative AI for Executives and Business Leaders specialization as well. Additionally, you should also be familiar with basic AI terminology and common AI business concepts.<br />  </div><h1>Learning Objectives</h1><ul><li>Steps to formulate genAI business applications for your organization </li><li>How to mitigate AI implementation risks </li><li>Best practices for ensuring valid, useful AI output </li><li>Factors to consider when evaluating AI implementation feasibility </li></ul><div><br /></div><h1>Course Syllabus</h1><div><strong>Module 1: Develop an AI Use Case for Your Organization</strong></div><ul><li>Module Introduction and Objectives</li><li>Video: Welcome to the Course</li><li>Video: Writing Effective Prompts </li><li>Video: Approaches to writing prompts</li><li>[Optional] Lab: Getting to Know Our AI Prompting Tool  </li><li>Lab: Explore prompt writing </li><li>Video: Writing Strategic Use Cases</li><li>Video: Risk Awareness and Mitigation with GenAI</li><li>Reading: Case study: Integrating AI Successfully </li><li>Reading: Case study:  An Unsuccessful Chatbot Implementation</li><li>Lab: Formulating a Business Goal with AI Assistance </li><li>Video: Evaluating AI Output</li><li>Lab: Lab: Finalize Your Feasible Use Case</li><li>Reading: Case study: Misguided Marketing Campaign</li><li>Reading or potential SME video: TBD</li><li>Course  Summary</li></ul><div><strong>Module 2: Final Quiz and Course Wrap-up</strong></div><ul><li>Graded Quiz for Certification: Final Quiz </li><li>Reading: Congratulations and Next Steps </li><li>Reading: Additional Reading Recommendations </li><li>Reading: Thanks from the Course Team </li></ul><h1>Recommended Skills Prior to Taking this Course</h1><ul><li>Basic AI terminology concepts related to enterprise AI implementation </li><li>Common issues and risks associated with AI implementation in enterprise </li><li>Common genAI capabilities and applications</li></ul>
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GenAI for Execs & Business Leaders: Formulate Your Use Case

Build a genAI use case for your organization using your industry expertise and, in the process, gain experience with AI tools and best practices. 

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  <div>The R programming language is purpose-built for data analysis. R is the key that opens the door between the problems that you want to solve with data and the answers you need to meet your objectives. This course starts with a question and then walks you through the process of answering it through data. You will first learn important techniques for preparing (or wrangling) your data for analysis. You will then learn how to gain a better understanding of your data through exploratory data analysis, helping you to summarize your data and identify relevant relationships between variables that can lead to insights. Once your data is ready to analyze, you will learn how to develop your model and evaluate and tune its performance. By following this process, you can be sure that your data analysis performs to the standards that you have set, and you can have confidence in the results. You will build hands-on experience by playing the role of a data analyst who is analyzing airline departure and arrival data to predict flight delays. Using an Airline Reporting Carrier On-Time Performance Dataset, you will practice reading data files, preprocessing data, creating models, improving models, and evaluating them to ultimately choose the best model.<br /><br /></div><div><br />Watch the videos, work through the labs, and add to your portfolio. Good luck!<br /><br /></div><div><strong><br />Note:</strong> The pre-requisite for this course is basic R programming skills. For example, ensure that you have completed a course like Introduction to R Programming for Data Science from IBM.<br /><br /></div>
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Data Analysis with R

Learn to use R Programming for data analysis and build hands-on experience with an airline dataset. Pre-requisite: basic R programming skills.

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  <div><br /><br /></div><h1>Course Syllabus</h1><div><strong>Abstract</strong></div><div>This course offers a look into the expanding field of cybersecurity. It is designed to empower students with the knowledge required to find and secure a career in the industry. The course begins by addressing the widening employment gap in cybersecurity, detailing the various roles and responsibilities associated with different career levels. The second module highlights the importance of technical and soft skills, providing a balanced approach to professional development. In the third module, the course delves into the beneficial role of certifications. The  lessons provide an overview of the most recognized certifications in the field and explain the preparation process for certification exams. </div><div><strong>Course Learning Objectives</strong></div><div>After completing this course, a learner will be able to:</div><ul><li>Identify cybersecurity careers and career paths</li><li>Identify the different skills required for cybersecurity careers</li><li>Describe the available cybersecurity certifications </li></ul><div><strong>Course Description</strong></div><div><strong>Course Outline</strong></div><ul><li>Module 1: Cybersecurity Careers</li><li>Module 2: Cybersecurity Skills </li><li>Module 3: Cybersecurity Certifications</li><li>Module 4: Final Assessment and Final Project</li></ul><h1><strong>Module 1 - </strong>Cybersecurity Careers</h1><div><strong>Description</strong></div><div>In this module, learners will learn about various cybersecurity career roles,  typical responsibilities, and salary potential. Learners will also learn about the typical path to those cybersecurity careers.</div><div><strong>Objectives</strong></div><div>By the end of this week, you will be able to:</div><ul><li>Explain the cybersecurity field. </li><li>Describe the steps needed to get into the cybersecurity field. </li><li>Compare the roles and responsibilities of various careers within the cybersecurity field. </li></ul><div><strong>Topics</strong></div><ul><li>Cybersecurity field  </li><li>Entry-level careers and responsibilities </li><li>Mid-level careers and responsibilities </li><li>Advanced-level careers and responsibilities </li></ul><div><strong>Assets </strong></div><div><strong>Lesson 0: Welcome</strong></div><ul><li>Video: Course introduction</li><li>Video: Overview of Cybersecurity Specialist PC</li><li>Reading: How to make the most of this course</li><li>Reading: Course syllabus</li></ul><div><strong>Lesson 1: The cybersecurity field </strong></div><ul><li>Video: Why cybersecurity</li><li>Reading: A growing field </li><li>Video: Expert viewpoints: What is the best part about working in the cybersecurity field? </li><li>Video: How to get into the cybersecurity field    </li></ul><div><strong>Lesson 2: Entry-level careers and responsibilities </strong></div><ul><li>Video: Entry-level careers</li><li>Reading: Entry-level roles, responsibilities, and salaries</li><li>Video: Expert viewpoints: Getting started as a Cybersecurity Professional</li></ul><div><strong>Lesson 3: Mid-level careers and responsibilities </strong></div><ul><li>Video: Mid-level careers </li><li>Reading: Mid-level roles, responsibilities, and salaries</li><li>Video: Expert viewpoints: A Day in the Life of a Cybersecurity Professional</li></ul><div><strong>Lesson 4: Advanced-level careers and responsibilities </strong></div><ul><li>Video: Advanced-level careers</li><li>Reading: Advanced-level roles, responsibilities, and salaries </li><li>Activity: Cybersecurity career paths</li><li>Video: Expert viewpoints: Cybersecurity Career Path</li></ul><div><strong>Lesson 5: Module 1 Summary and Assessment </strong></div><ul><li>Reading: Module 1 Summary </li><li>Practice Quiz: Module 1 Practice quiz</li><li>Reading: Module 1 Glossary:   </li><li>Graded Quiz: Module 1 Quiz </li></ul><h1><strong>Module 2 - </strong>Cybersecurity Skills </h1><div><strong>Description</strong></div><div>In this module, learners will learn about the technical and soft skills that are required  to attain different levels of cybersecurity careers.</div><div><strong>Objectives</strong></div><div>By the end of this week, you will be able to:</div><ul><li>Explain the importance of technical skills in the cybersecurity field. </li><li>Identify technical skills important for a cybersecurity professional. </li><li>Explain the importance of soft skills in the cybersecurity field. </li><li>Identify soft skills important for a cybersecurity professional.</li></ul><div><strong>Topics</strong></div><ul><li>Technical skills </li><li>Soft skills </li></ul><div><strong>Assets</strong></div><div><strong>Lesson 1: Technical skills - IT fundamentals</strong></div><ul><li>Video: Importance of IT fundamentals technical skills</li><li>Reading: IT fundamentals technical skills facts   </li><li>Video: Expert viewpoints: Fundamental IT skills. </li></ul><div><strong>Lesson 2: Technical skills – Cybersecurity</strong></div><ul><li>Video: Importance of cybersecurity technical skills </li><li>Reading: Cybersecurity technical skills facts</li><li>Video: Expert viewpoints: Cybersecurity technical skills.</li></ul><div><strong>Lesson 3: Soft skills </strong></div><ul><li>Video: Importance of soft skills</li><li>Reading: Soft skills facts </li><li>Video: Expert viewpoints: Soft skills</li></ul><div><strong>Lesson 4: Module 2 Summary and Assessment </strong></div><ul><li>Reading: Skills to job role mapping (Table/Infographic)</li><li>Reading: Module 2 Summary </li><li>Practice Quiz: Module 2 Practice quiz</li><li>Reading: Module 2 Glossary:   </li><li>Graded Quiz: Module 2 Quiz</li></ul><h1><strong>Module 3 - </strong>Cybersecurity Certifications</h1><div><strong>Description</strong></div><div>In this module, learners will learn about the different levels of cybersecurity certifications available and learn how best to prepare to attain them. </div><div><strong>Objectives</strong></div><div>By the end of this week, you will be able to:</div><ul><li>Explain the benefits of industry certifications. </li><li>Identify certifications available for cybersecurity professionals. </li><li>Describe the process for preparing for and taking a certification exam. </li></ul><div><strong>Topics</strong></div><ul><li>Benefits of industry certifications</li><li>Cybersecurity certifications </li><li>Preparing for and taking a certification exam</li></ul><div><strong>Assets</strong></div><div><strong>Lesson 1: Benefits of Certifications </strong></div><ul><li>Video: Benefits of cybersecurity certifications</li><li>Reading: Cybersecurity certifications facts </li><li>Video Expert Viewpoints: Benefits of certifications.</li></ul><div><strong>Lesson 2: Certifications</strong></div><ul><li>Video:  Overview of Cybersecurity Certifications </li><li>Reading: CompTIA certifications </li><li>Reading: ISC2 certifications </li><li>Reading: Other certifications (include industry)</li><li>Video: Expert viewpoints: Impact of certifications. </li></ul><div><strong>Lesson 3: Preparing for a certification exam</strong></div><ul><li>Video: Choosing the right certification </li><li>Video: Study for the exam </li><li>Video: Schedule and take the exam </li><li>Video: Expert viewpoints: Tips for taking and passing a certification exam</li></ul><div><strong>Lesson 4: Module 3 Summary and Assessment</strong></div><ul><li>Reading: Module 3 Summary </li><li>Practice Quiz: Module 1 Practice quiz</li><li>Reading: Module 3 Glossary   </li><li>Graded Quiz: Module 3 Quiz</li></ul><div><br /></div><h1><strong>Module 4 - Course Summary and Final Assessment</strong></h1><div><strong>Description</strong></div><div>In this module, learners will get a summary of cybersecurity careers, skills, and certifications and perform a final graded assessment to assess their understanding of the course content.</div><div><strong>Objectives</strong></div><div>By the end of this week, you will be able to:</div><ul><li>Summarize cybersecurity careers</li><li>Summarize cybersecurity skills </li></ul><div><strong>Topics</strong></div><ul><li>Summary of cybersecurity careers</li><li>Summary of cybersecurity skills</li></ul><div><strong>Assets</strong></div><div><strong>Lesson 1: Course Summary and Final Assessment </strong></div><ul><li>Reading: Course Summary </li><li>Glossary: Cumulative </li><li>Graded Quiz: Final Assessment</li></ul><div><strong>Lesson 2: Course Wrap-Up</strong></div><ul><li>Video: Expert Viewpoints: Tips for a Cybersecurity Career</li><li>Reading: Congratulations and Next Steps </li><li>Reading: Thanks from the Course Team</li></ul><div><strong>Lesson 3: [Honors] Final Project</strong></div><ul><li>Reading: About this optional final project</li><li>Final Project: Cybersecurity Career Research</li></ul>
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Launch Your Cybersecurity Career

Discover the fundamentals of cybersecurity careers in this beginner-level course. Learn essential skills like cybersecurity principles and information security practices. Explore pathways in cybersecurity, focusing on CompTIA and ISC2 certifications. Gain foundational knowledge to start your journey in the security field.

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  <ul><li>In this course, you will explore the fundamental principles of cryptography and encryption that are crucial for safeguarding sensitive data in today's digital landscape. You'll learn about various cryptographic algorithms, encryption techniques, and the implementation of secure systems using industry-standard tools such as OpenSSL. The course covers theoretical concepts and practical applications, from basic encryption methods to advanced topics such as blockchain technology and quantum-safe encryption.<br />This course is part of the IBM Cybersecurity Fundamentals Professional Certificate, designed to equip learners with essential skills and hands-on experience to excel in cybersecurity.<br /><strong>Prerequisites and hardware requirements</strong><br />This course requires basic knowledge of cybersecurity concepts and IT fundamentals including computer networks and storage. You will need a laptop or desktop computer to get the most out of this course. Prior knowledge of Linux/shell commands is beneficial but not absolutely necessary.<br /><strong>Course objectives</strong><br />After completing this course, you will be able to:<ul><li>Explain fundamental concepts of cryptography and implement various encryption techniques</li><li>Apply symmetric and asymmetric encryption algorithms using industry-standard tools</li><li>Analyze cryptographic attacks and assess system vulnerabilities</li><li>Evaluate and implement public key infrastructure (PKI) components for secure communications</li></ul></li><li><strong>Course outline</strong><br />This course has four modules, which are listed below. We encourage you to set aside a few weekly hours to complete all modules successfully. Consistency will help you achieve your learning goals! <br />You will benefit from viewing all videos and readings and solidifying that knowledge by completing all of the activities. To get the most value from this course, watch all videos, complete the readings, and participate in the hands-on labs. Test your knowledge through practice and graded quizzes and demonstrate your skills in the final project.<br /><strong>Module 1: Cryptography Algorithms and Their Classifications</strong><br />In this module, you'll master the foundational concepts of cryptography and its role in data security. You'll learn to classify cryptographic algorithms, conduct cryptanalysis using OpenSSL, and understand both asymmetric and symmetric cryptography principles. The module covers essential algorithms such as RSA, ECC, and AES, along with key exchange protocols such as Diffie-Hellman, all supported by hands-on practical exercises.<br /><strong>Module 2: Encryption Techniques and Public Key Infrastructure (PKI) </strong><br />This module focuses on practical encryption techniques and PKI fundamentals. You'll explore various encryption methods, including full disk, partition, file, and volume encryption. You'll learn about transport encryption, PKI principles, and the roles of Certificate Authorities. The module also covers hardware security elements such as TPM and HSM and important obfuscation techniques such as steganography and tokenization.<br /><strong>Module 3: Hashing, Digital Signatures, Blockchain and Digital Certificates </strong><br />In this module, you'll learn about hashing algorithms, digital signatures, and blockchain technology. You'll implement secure hashes using OpenSSL, create and verify digital signatures, and understand blockchain applications. The module also covers digital certificates, OCSP, and certificate management, providing a comprehensive understanding of modern cryptographic applications.<br /><strong>Module 4: Final Project and Course Wrap-up </strong><br />In the final module, you'll apply your knowledge to real-world scenarios by implementing cryptographic techniques. The project will test your ability to secure systems using appropriate encryption methods, manage digital certificates, and implement secure communication. The course concludes with a comprehensive review and final assessment.<br />Congratulations on taking this step to enhance your cybersecurity knowledge! We look forward to guiding you through the fascinating world of encryption and cryptography.</li></ul><div><br /></div>
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Encryption and Cryptography Essentials

Discover intermediate cryptography and encryption techniques in a security-focused course designed to enhance your understanding and skills. You will explore key concepts including algorithm analysis, secure communication protocols, and data protection strategies. Improve your ability to implement cryptographic solutions and develop a robust security approach. Equip yourself with essential knowledge to safeguard digital information effectively.

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  <h1><strong>📖 The Story Behind DocChat: Why We Built This</strong></h1><div>Imagine you're a researcher, a lawyer, or a data analyst working with hundreds of pages of documents—contracts, compliance reports, technical papers, or financial statements. You need specific information, but finding the right details means spending hours skimming through text, tables, and appendices.<br /><br /></div><div>You try ChatGPT or DeepSeek, expecting an easy solution. But instead of pulling answers directly from your documents, these models hallucinate responses, give vague summaries, or simply fail to interpret structured data like tables and figures. The frustration grows—why can’t AI just read the document and tell me the answer?<strong><br /></strong><br /></div><div>That’s exactly the problem <strong>DocChat</strong> solves.<br /><br /></div><div>DocChat isn’t just another chatbot—it’s a <strong>multi-agent Retrieval-Augmented Generation (RAG) system</strong> that retrieves <strong>relevant document sections</strong>, generates answers, and <strong>verifies them for accuracy</strong>. Unlike traditional LLMs, DocChat won’t guess—it will <strong>either provide a fact-based answer or let you know if the question is out of scope</strong>.<br /><br />
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<br /><br /></div><h1><strong>🚀 Introduction: Why This Project Matters</strong></h1><div><br /></div><div>The rise of LLMs and RAG pipelines has transformed how we interact with knowledge, but most RAG systems are naïve—they retrieve documents without checking if the response is correct. This leads to hallucinated answers, incomplete reasoning, and unreliable outputs.<br /><br /></div><div>That’s where <strong>multi-agent AI workflows</strong> come in. Instead of relying on a single model, DocChat distributes tasks across multiple agents:</div><ul><li><strong>A Hybrid Retriever</strong> that uses <strong>BM25 and vector search</strong> to find the right content—even across multiple documents.</li><li><strong>A Research Agent</strong> that <strong>understands the query</strong> and generates a structured answer.</li><li><strong>A Verification Agent</strong> that <strong>cross-checks the answer against the original document</strong> and flags hallucinations.</li><li><strong>A Self-Correction Mechanism</strong> that reruns the research process if verification fails.</li></ul><div>This means that <strong>DocChat is more reliable than general-purpose chatbots</strong>—it doesn’t just generate an answer, it <strong>ensures accuracy</strong> before responding.<br /><br />
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 <br /><br /></div><h1><strong>🎯 What You'll Learn in This Hands-On Project</strong></h1><div><br /></div><div>This step-by-step tutorial will guide you through building DocChat, a multi-agent RAG system with LangGraph, Docling, and ChromaDB. By the end, you’ll understand how to:</div><ul><li><strong>Build and deploy a multi-agent RAG pipeline</strong> using LangGraph.</li><li><strong>Implement hybrid retrieval</strong> (BM25 + vector search) for accurate document search.</li><li><strong>Extract structured text from complex PDFs</strong> using <strong>Docling</strong>.</li><li><strong>Verify AI-generated responses</strong> to prevent hallucinations and improve accuracy.</li><li><strong>Create an interactive web UI</strong> with <strong>Gradio</strong> to make your system accessible.</li></ul><div><br /><br /></div><h1><strong>👥 Who Should Take This Project?</strong></h1><div><br /></div><div>This project is <strong>perfect for</strong>:</div><ul><li>AI and NLP enthusiasts wanting to explore multi-agent workflows and RAG pipelines.</li><li>Data Scientists &amp; AI Engineers who want to build production-ready AI assistants.</li><li>Developers working with enterprise search systems and document intelligence solutions.</li><li>Researchers &amp; analysts who need reliable AI-assisted document querying tools.</li><li>Anyone frustrated with chatbots that hallucinate answers and need better fact-checking AI.<br /><br /></li></ul><div><br /></div><h1><strong>🛠️ What You Need (Prerequisites)</strong></h1><div><br /></div><div>Before diving in, you should have:</div><ul><li>Basic Python knowledge – If you can write Python scripts and install libraries, you're good.</li><li>Familiarity with LLMs &amp; RAG – A basic understanding of retrieval-augmented generation is helpful but not required.</li><li>Experience with LangChain (optional) – Since we use LangGraph, knowledge of LangChain is a plus but not mandatory.</li><li>Some exposure to AI workflows (optional) – If you’ve ever built an AI pipeline or chatbot, this will be easier to grasp.</li><li>A modern web browser (e.g., Chrome, Firefox, Edge, or Safari).</li></ul><div><br /></div><div><br /></div>
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Chat with your documents via Agentic RAG, LangGraph, Docling

Leverage a multi-agent RAG tool to create a dynamic, AI-driven information retrieval system using LangGraph, Docling, and self-correction mechanisms. This guided project helps you automate data analysis, optimize decision-making, and reduce manual effort while enhancing accuracy. Discover how multi-agent architecture enables a seamless, adaptive framework that continuously learns and improves, making it ideal for research, business intelligence, and automated data processing. Transform your workflows with cutting-edge AI-powered retrieval techniques!

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  <div>Unlock the power of Word2Vec for natural language processing in this hands-on project. Learn to implement and train Continuous Bag of Words (CBOW) and Skip-gram models using PyTorch, mastering how word embeddings represent relationships between words. Additionally, explore GloVe (optional but fun to learn) as another powerful technique for generating word embeddings, broadening your understanding of NLP. With practical coding exercises, this project helps you understand the principles behind Word2Vec and introduces GloVe, foundational NLP techniques used in search engines, recommendation systems, and sentiment analysis.</div><div><br />------------------------------------------------------------------------------------------------------------------<br /><br /></div><h1>Why This Topic Is Important:</h1><div>Word embeddings are crucial for understanding and processing text data in real-world applications, from chatbots to text classification systems. This project offers a clear and practical introduction to Word2Vec while also introducing GloVe, giving you insights into two widely used techniques in NLP workflows. By completing this project, you’ll gain the foundational knowledge to use word embeddings effectively and enhance your ability to tackle language-driven challenges in AI and machine learning.<br /><br />------------------------------------------------------------------------------------------------------------------<br /><br /></div><h1>A Look at the Project Ahead:</h1><div>In this project, you’ll implement Word2Vec models using PyTorch and understand the mechanics of CBOW and Skip-gram architectures. You’ll also have the opportunity to explore GloVe as an optional but valuable technique for generating word embeddings. Dive into the mathematical foundations and coding implementation of word embeddings while exploring their applications in NLP.</div><div>By the end of the project, you will:</div><ul><li>Understand the principles of Word2Vec and the role of CBOW and Skip-gram models in generating word embeddings.</li><li>Learn to train Word2Vec models from scratch using PyTorch, equipping you with hands-on experience in building and testing embeddings.</li><li>Explore how word embeddings capture semantic relationships between words for improved text representation in machine learning tasks.</li></ul><div><br /></div><div>------------------------------------------------------------------------------------------------------------------<br /><br /></div><h1>What You’ll Need</h1><div>To successfully complete this project, you’ll need:</div><ul><li>A basic understanding of Python programming and experience with PyTorch.</li><li>Familiarity with foundational concepts in natural language processing.</li><li>A web browser to access tools and execute code.</li></ul><div>This project is perfect for NLP enthusiasts and professionals looking to deepen their understanding of word embeddings while enhancing their machine learning skills.</div>
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Master Word Embeddings from Scratch with Word2Vec & PyTorch

Learn to create word embeddings from scratch using Word2Vec and PyTorch. In this project, you'll implement Continuous Bag of Words (CBOW) and Skip-gram models, essential for Natural Language Processing (NLP) tasks. Gain a deep understanding of how word embeddings represent text data, enabling better context and meaning extraction. This hands-on project focuses on building foundational skills in NLP, empowering you to better understand and apply word embedding techniques to real-world text-processing tasks.

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  <div><strong>Dive into Text Splitting Mastery with LangChain!<br /></strong><br />Unlock the power of efficient text processing and elevate your AI projects with LangChain's text splitting techniques! This guided project explores the core of text chunking, helping you optimize large language models (LLMs) by breaking down complex documents into manageable pieces. Through hands-on exercises, you’ll learn how to split text effectively to enhance accuracy and performance across various NLP applications.<br /><br /></div><div><strong>What You'll Learn<br /></strong><br /></div><ul><li><strong>Fundamentals of Text Splitting:</strong> Grasp the principles of text chunking and why it's essential for working with large datasets in LLMs.</li><li><strong>LangChain Techniques:</strong> Leverage LangChain's text splitting utilities to preprocess and structure input data for optimal performance.</li></ul><div><br /></div><div><br /></div><div><strong>Why It Matters<br /></strong><br /> Text splitting is the backbone of many advanced AI workflows. By mastering this skill, you can:<br /><br /></div><ul><li><strong>Boost Model Performance:</strong> Ensure that your LLMs handle complex documents more efficiently by working with well-structured text chunks.</li><li><strong>Enhance Scalability:</strong> Break down extensive datasets, enabling smoother processing and reducing memory constraints.</li><li><strong>Drive Better Insights:</strong> Improve the granularity of model outputs, leading to more precise and context-aware responses.</li></ul><div><br /></div><div><strong>Who Should Register<br /></strong><br /> This project is ideal for:<br /><br /></div><ul><li><strong>Data Scientists and NLP Engineers:</strong> Professionals working on LLM optimization and text processing.</li><li><strong>Developers:</strong> Those interested in enhancing AI workflows through smarter data handling techniques.</li><li><strong>Business Analysts:</strong> Individuals looking to leverage text data for strategic insights and operational efficiency.</li></ul><div><br /></div><div><strong>What You'll Need<br /></strong><br /> Before starting, ensure you have:<br /><br /></div><ul><li>Basic knowledge of Python programming.</li><li>A computer with a modern web browser (Chrome, Edge, Firefox, or Safari).</li></ul><div><br /></div><div>Embark on this transformative journey and harness the potential of LangChain to push the boundaries of your AI projects!</div>
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The Ultimate Guide to Text Splitting with LangChain for LLMs

Discover text splitting techniques with LangChain's CharacterTextSplitter and RecursiveCharacterTextSplitter for efficient information retrieval. This guided project empowers you to transform lengthy documents into contextually coherent segments, enhancing data processing for researchers and developers. Gain hands-on experience in customizing chunk sizes and separators to optimize document segmentation, making retrieval tasks precise and contextually relevant in just 30 minutes.

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  <div><br /></div><h1>Course Syllabus</h1><div><br /></div><h1>Module 1</h1><ul><li>Introduction to Neural Networks and Deep Learning</li></ul><div>In this module, you will learn about exciting applications of deep learning and why now is the perfect time to learn deep learning. You will also learn about neural networks and how most of the deep learning algorithms are inspired by the way our brain functions and the neurons process data. Finally, you will learn about how neural networks feed data forward through the network.<br /><br /></div><h1>Module 2</h1><ul><li>Artificial Neural Networks<br /><br />In this module, you will learn about the gradient descent algorithm and how variables are optimized with respect to a defined function. You will also learn about backpropagation and how neural networks learn and update their weights and biases. Futhermore, you will learn about the vanishing gradient problem. Finally, you will learn about activation functions.<br /><br /></li></ul><h1>Module 3</h1><ul><li>Keras and Deep Learning Libraries</li></ul><div><br /></div><div>In this module, you will learn about the different deep learning libraries namely, Keras, PyTorch, and TensorFlow. You will also learn how to build regression and classification models using the Keras library.<br /><br /></div><div><br /></div><h1>Module 4</h1><ul><li>Deep Learning Models</li></ul><div><br /></div><div>In this module, you will learn about the difference between the shallow and deep neural networks. You will also learn about convolutional networks and how to build them using the Keras library. Finally, you will also learn about recurrent neural networks and autoencoders.<br /><br /></div><div><strong>Course Project<br /><br /></strong>In this module, you will conclude the course by working on a final assignment where you will use the Keras library to build a regression model and experiment with the depth and the width of the model.<br /><br /></div>
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Introduction to Deep Learning & Neural Networks with Keras

Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks? You will learn about the different deep learning models and build your first deep learning model using the Keras library.Upon course completion, learners will gain the capacity to describe neural networks and deep learning models, as well as discern the distinctions between them. 

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  <div>The emergence of <strong>DeepSeek</strong> has sent ripples through the global AI community. DeepSeek R1 is a powerful open-source language model that rivals leading proprietary LLMs while remaining freely accessible. Its open framework empowers developers, researchers, and businesses to innovate in automation, content generation, and AI applications without restrictive costs.</div><div><br /></div><div>Unlike <strong>AI models that require paid subscriptions or have restricted usage rights</strong>, DeepSeek R1 offers full transparency, allowing users to modify and adapt it for greater control, customization, and scalability. As a <strong>reasoning model</strong>, it provides <strong>step-by-step explanations</strong> for complex queries, making it highly effective for coding, problem-solving, and decision-making.</div><div><br /></div><div>Positioned as an alternative to <strong>OpenAI’s o1 and o3</strong>, DeepSeek R1 delivers <strong>comparable or superior performance</strong> in key areas, combining open accessibility with advanced reasoning capabilities.</div><div><br /><br /></div><h1>Why This Topic Is Important</h1><div>Using open-source LLMs like DeepSeek R1 is valuable for developers and AI enthusiasts. Gaining hands-on experience with this technology makes it possible to integrate advanced AI capabilities into a variety of applications, from natural language processing to automated content generation. This project provides a structured approach to working with DeepSeek R1, helping build proficiency in modern AI toolsets.<br /><br /></div><h1>A Look at the Project Ahead</h1><ul><li>Learn how to set up and authenticate with the <strong>DeepSeek R1 API </strong>using Together.ai, ensuring secure and efficient access to the model's capabilities.</li><li>Explore parameter tuning to customize the model's responses, tailoring its output to specific tasks and requirements.</li></ul><div><br /></div><h1>What You'll Need</h1><div>Before starting this project, ensure you have:</div><ul><li><strong>Technical Requirements:</strong> A basic understanding of Python programming, including familiarity with libraries such as os for environment variable management.</li><li><strong>Browser Setup:</strong> A web browser for accessing tools and running code.</li></ul><div>By the end of this project, you'll be equipped with the knowledge and skills to effectively implement DeepSeek R1 in your applications, opening doors to innovative AI-driven solutions.</div>
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How to Get Started with Deepseek: A Beginner's Guide

Explore the capabilities of DeepSeek R1, an open-source AI model with advanced reasoning that rivals top LLMs like OpenAI’s o1 and o3. This hands-on project is perfect for developers and beginners, covering how to fine-tune parameters, handle streaming responses, and make structured API calls for better AI-powered outputs across various tasks. Learn to generate text, retrieve code snippets, and process real-time outputs with clear step-by-step instructions. Whether you’re integrating LLMs into apps or optimizing workflows, this guide makes working with DeepSeek simple and effective.

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  <div>In the world of natural language processing (NLP), the ability to make machines understand and generate human language has reached unprecedented levels. At the heart of this revolution are Transformer models—the engines behind systems like Google Translate, Gemini, and GPT—that allow computers to excel at tasks like translation, summarization, and even generating text.<br /><br /></div><div>By the time you finish this tutorial, you’ll have built your very own Transformer model from the ground up. You'll understand how to prepare text for a machine to process, and you'll implement key components like self-attention and positional encoding, the very techniques that give Transformer models their edge. These are the same principles that make it possible for AI to comprehend and generate text in ways that feel almost human.<br /><br />
      <img src="https://author.skills.network/rails/active_storage/representations/redirect/eyJfcmFpbHMiOnsiZGF0YSI6MzA2MDMsInB1ciI6ImJsb2JfaWQifX0=--46d9e15a315c4db7fff56dccc6bb28479337170d/eyJfcmFpbHMiOnsiZGF0YSI6eyJmb3JtYXQiOiJwbmciLCJsb2FkZXIiOnsibiI6LTF9LCJyZXNpemVfdG9fbGltaXQiOlsxMDI0LDc2OF19LCJwdXIiOiJ2YXJpYXRpb24ifX0=--d5cef5cc4423e679c464df97b6ff7a8fa3dec7ab/Transformer(1).png" />

  
      By the end of this project, you will understand the attention mechanism behind the Transformer architecture.
  
</div><div><br /></div><h1>A Look at the Project Ahead</h1><div>In this guided project, you’ll learn how to:</div><ul><li>Understand tokenization and one-hot encoding to prepare textual data for machine learning models.<br /><br /></li><li>Implement the self-attention mechanism and integrate it into a simple neural network model.<br /><br /></li><li>Apply positional encoding to capture word order within sequences, improving the model’s understanding of text structure.<br /><br /></li><li>Build a basic translation model or text processing task, applying the key concepts of self-attention and positional encoding in practice.<br /><br /></li><li>Compare Transformers to traditional sequence models like RNNs and LSTMs, gaining insight into the advantages of modern architectures.</li></ul><div><br /><br /></div><h1>Who should complete this project?</h1><div>This project is ideal for:<br /><br /></div><ul><li>Aspiring NLP Engineers and Researchers</li><li>Machine Learning Practitioners</li><li>Data Scientists Exploring Deep Learning</li><li>Software Developers Interested in Text Processing</li></ul><div><br /><br /></div><h1>What You'll Need</h1><div>Before starting this project, ensure you have the following:<br /><br /></div><ul><li><strong>Basic Python Programming:</strong> You should be comfortable writing Python code, as we’ll be implementing key components of the Transformer model using Python libraries.<br /><br /></li><li><strong>Familiarity with Neural Networks:</strong> A foundational understanding of neural networks, especially feedforward networks, will be helpful as you build and experiment with the model architecture.<br /><br /></li><li><strong>Introduction to Machine Learning Concepts:</strong> While we’ll go over key concepts, having some prior exposure to machine learning, especially how models are trained and evaluated, will make the project smoother.<br /><br /></li><li><strong>A current version of a web browser: </strong>To run the project and test the chatbot interface, you’ll need a web browser like Chrome, Edge, Firefox, or Safari.<br /><br /></li></ul><div><br /></div><div>Don't worry if you're not an expert in NLP or Transformers yet! This project is designed to guide you through the core concepts step-by-step. Just bring your enthusiasm and a willingness to experiment and learn!</div>
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Understanding Attention Mechanism and Positional Encoding

Master tokenization, one-hot encoding, self-attention, and positional encoding to build NLP models using Transformer architectures. In this tutorial, you will explore the core concepts of Transformer models and understand their application in natural language processing. You’ll implement a basic self-attention mechanism, integrate it into a neural network, and apply positional encoding to improve sequence understanding.

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  <div>Imagine you are an AI engineer who wants to train a "Happy LLM" and a "Pessimistic LLM" to train customer service agents. You have a reward function trained on the sentiment classifier from the IMDb dataset, and you will now use Reinforcement Learning (RL). RL is a subfield of machine learning where an agent learns to make decisions by performing actions in an environment to maximize a cumulative reward. The agent, in this case, will be the LLM, and the decisions will be about what text to output. Unlike supervised learning, which requires labeled input/output pairs, RL relies on the agent exploring the environment and learning from the feedback it receives in the form of rewards or penalties. This trial-and-error approach enables the agent to improve its decision-making strategy over time.<br /><br /></div><div>Proximal Policy Optimization (PPO) is one of the most effective and widely used RL algorithms. Introduced by OpenAI, PPO strikes a balance between simplicity and performance, making it a popular choice for training RL agents. PPO optimizes the policy directly and employs mechanisms to ensure the updates are not too drastic, thereby maintaining stability and reliability during training.<br /><br /></div><div>In this lab, you will be guided through the process of training an RL agent using the PPO algorithm with a focus on sentiment analysis. You will use the IMDb dataset, a large collection of movie reviews, to train your model. By the end of this lab, you will have a solid understanding of how to implement and train an RL agent using PPO, and you will be equipped with practical skills to apply RL techniques to other problems and datasets. <br /><br /><br /></div><h1>A Look at the Project Ahead</h1><div>After completing this lab you will be able to:<br /><br /></div><ul><li>Apply the basics of reinforcement learning and proximal policy optimization (PPO).<br /><br /></li><li>Set up the environment and load the IMDb dataset for training.<br /><br /></li><li>Define and configure the PPO agent and tokenizer.<br /><br /></li><li>Implement the PPO training loop.<br /><br /></li><li>Generate and evaluate text responses from the trained model.<br /><br /></li><li>Compare the performance of two models on the dataset.<br /><br /></li><li>Save and load the trained model for future use.</li></ul><div><br /><br /></div><h1>Who should complete this project?</h1><div>This project is designed for:<br /><br /></div><ul><li>Aspiring AI and Machine Learning Engineers</li><li>NLP Enthusiasts</li><li>Data Scientists Looking to Enhance Skills in RL</li><li>AI Developers Interested in Customer Experience Applications<strong><br /></strong><br /></li></ul><div><br /></div><h1>What You'll Need</h1><div>Before starting this project, make sure you have the following:<br /><br /></div><ul><li><strong>Browser</strong>: The IBM Skills Network Labs platform is compatible with the latest versions of Chrome, Edge, Firefox, and Safari. Please ensure your browser is up-to-date for the best experience.<br /><br /></li><li><strong>Python Knowledge</strong>: You should have a basic understanding of Python, including working with libraries and data structures.<br /><br /></li><li><strong>Familiarity with Machine Learning</strong>: A foundational understanding of machine learning concepts, particularly reinforcement learning, will help you navigate through the lab efficiently.<br /><br /></li><li><strong>Basic Knowledge of NLP</strong>: It's helpful if you are familiar with natural language processing (NLP) concepts, especially sentiment analysis.</li></ul><div><br />Take the next step in your AI journey and dive into the world of reinforcement learning with PPO. By the end of this lab, you’ll have practical skills in training "Happy" and "Pessimistic" LLMs using sentiment analysis. Whether you're building customer service agents or exploring advanced NLP techniques, this project will equip you with the tools and knowledge to take your AI projects to the next level.<br /><br /></div>
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Train Sentiment-Aware LLMs with Reinforcement Learning & PPO

Explore the integration of reinforcement learning with human feedback using Proximal Policy Optimization (PPO) to build sentiment-aware LLMs. In this hands-on project, you’ll build "Happy" and "Pessimistic" LLMs by leveraging sentiment analysis with IMDb data. By the end, you will have trained, evaluated, and compared models capable of delivering sentiment-driven responses, providing practical experience with reinforcement learning for real-world applications.

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  <div>Gain the real-world experience of cybersecurity analysis employers look for with this hands-on capstone course.<br /><br />In this course, you’ll analyze real-life breach response case studies in areas such as phishing, vishing, point of sale (PoS) breaches, insider threats, AI-related breaches, and ransomware attacks. <br /><br />You’ll discover how attackers exploit vulnerabilities and how organizations respond and prevent future incidents.<br /><br />Additionally, you’ll gain expertise in incident response, digital forensics, penetration testing, and compliance through engaging case study analysis. You’ll complete several hands-on activities to assess response strategies and propose improvements. <br /><br />The course concludes with a final project, where you’ll select a real-world data breach, identify vulnerabilities, and assess the root cause, actions taken, and response strategies. Plus, a structured template will guide you to critically record observations and propose actionable improvements for breach management. This capstone course is a fantastic experience to talk about in interviews!<br /><br />If you’re keen to add practical cybersecurity experience to your resume, enroll today to complete this valuable capstone project in just six weeks!</div><h1>Course Syllabus</h1><div><strong>Module 0: Welcome</strong></div><ul><li>Video: Course Introduction </li><li>Reading: Helpful Tips for Course Completion</li><li>General Information</li><li>Learning Objectives and Syllabus</li><li>Grading Scheme</li></ul><div><strong>Module 1: Analyzing Case Study Layouts &amp; Phishing Case Studies </strong></div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Exploring Case Studies </li><li>Video: How to Analyze Case Studies </li><li>Reading: Effective Layout for Analyzing Case Studies </li><li>Video: Overview of Phishing Scams </li><li>Reading: Can you spot the Difference</li><li>Reading: Cisco Cyberthreat Trends Report </li><li>Video: Phishing Case Study: Google &amp; Facebook </li><li>Reading: Analyzing the Facebook and Google Case Study</li><li>Video: Vishing Case Study: South Korean Doctor</li><li> Activity: Identifying and Responding to Phishing Attacks</li><li>Practice Quiz: Analyzing Case Study Layouts &amp; Phishing Case Studies </li><li>Reading: Summary and Highlights: Analyzing Case Study Layouts &amp; Phishing Case Studies </li><li>Graded Assignment: Graded Quiz: Analyzing Case Study Layouts &amp; Phishing Case Studies </li></ul><div><strong>Module 2: Analyzing PoS &amp; Insider Breach Case Studies </strong></div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Overview of PoS Breaches </li><li>Video: PoS Malware and Skimming </li><li>Video: PoS Case Study: Target  </li><li>Reading: Cyber Kill Chain Analysis of the Target Breach</li><li>Video: Home Depot Point of Sale (POS) Case Study</li><li>Activity: Analyzing the Home Depot Case Study </li><li>Video: Overview of Insider Breaches </li><li>Video: Insider Threat Detection</li><li>Reading: Mitigating Insider Threats</li><li>Video: Insider Breach Case Study: Vault 7 </li><li>Video: Insider Breach Case Study: Pegasus Airlines</li><li>Activity: Analyzing the Pegasus Airlines Case Study </li><li>Practice Quiz: Analyzing PoS &amp; Insider Breach Case Studies </li><li>Reading: Summary and Highlights: Analyzing PoS &amp; Insider Breach Case Studies </li><li>Graded Quiz: Analyzing PoS &amp; Insider Breach Case Studies </li></ul><div><strong>Module 3: Analyzing AI-Related Breaches and Ransomware Case Studies </strong></div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Overview of AI-Related Breaches </li><li>Reading: The Emerging Impact of AI on Data Breaches  </li><li>Video: AI-Related Breach Case Study: Deepfake Conference Call</li><li>Activity: Analyzing the Deep Fake Case Study </li><li>Video: Notable AI-Related Breaches </li><li>Video: Overview of Ransomware Breaches </li><li>Reading: Emerging Trends in Ransomware </li><li>Video: Ransomware Case Study: City of Atlanta </li><li>Video: Ransomware Case Study: Colonial Pipeline </li><li>Activity: Navigating through a Ransomware Attack</li><li>Practice Quiz: Analyzing AI-Related Breaches and Ransomware Case Studies </li><li>Reading: Summary and Highlights: Analyzing AI-Related Breaches and Ransomware Case Studies </li><li>Graded Quiz: Analyzing AI-Related Breaches and Ransomware Case Studies</li></ul><div><strong>Module 4: Analyzing Incident Response and Digital Forensics Case Studies </strong></div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Overview of Incident Response </li><li>Video: Incident Response Case Study: Google Home</li><li>Video: Incident Response Case Study: Lightning Strikes </li><li>Activity: Analyzing the Lightning Strikes Case Study </li><li>Reading: When Incident Response Goes Wrong</li><li>Video: Overview of Digital Forensics </li><li>Video: Digital Forensics Case Study: Bernard Madoff</li><li>Video: Digital Forensics Case Study: U.S. v. Ganias</li><li>Activity: Responding to a Digital Forensics Warrant</li><li>Reading: Digital Forensics Gone Wrong </li><li>Practice Quiz: Analyzing Incident Response and Digital Forensics Case Studies </li><li>Reading: Summary and Highlights: Analyzing Incident Response and Digital Forensics Case Studies </li><li>Graded Quiz: Analyzing Incident Response and Digital Forensics Case Studies</li></ul><div><strong>Module 5: Analyzing Penetration Testing and Compliance Case Studies </strong></div><ul><li>Module Introduction and Learning Objectives</li><li>Video: Overview of Penetration Testing </li><li>Video: Penetration Testing Case Study: Equifax </li><li>Video: Penetration Testing Case Study: X-Force Red  </li><li>Activity: Analyzing the IBM X-Force Red Case Study </li><li>Video: Overview of Compliance </li><li>Video: Compliance Case Study: Marriott </li><li>Video: Compliance Case Study: Equiniti</li><li>Activity: Analyzing the Equiniti Case Study </li><li>Practice Quiz: Analyzing Penetration Testing and Compliance Case Studies </li><li>Reading: Summary and Highlights: Analyzing Penetration Testing and Compliance Case Studies </li><li>Graded Quiz: Analyzing Penetration Testing and Compliance Case Studies </li></ul><div><strong>Module 6: Final Project</strong></div><ul><li>Module Introduction and Learning Objectives</li><li>Reading: About the Course Project </li><li>Video: Introduction to the Peer-to-Peer Applied Project: Analyzing a Data Breach  </li><li>Final Project: Applied Project: Analyzing a Data Breach  </li><li>Final Project: Submission and Evaluation </li><li>Peer-Review</li></ul><div><strong>Module 7: Course Wrap Up</strong></div><ul><li>Video: Course Wrap-up </li><li>Reading: Congratulations and Next Steps</li><li>Reading: Team and Acknowledgments</li><li>Copyrights and Trademarks</li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>Prior knowledge of network security, threat hunting, incident response, penetration testing, and digital forensics is recommended.</div>
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Cybersecurity Capstone and Case Studies

Get the practical cybersecurity analysis experience employers look for with this immersive, hands-on capstone course. 

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  <div>In today’s digital world, where children have easy access to a vast array of music, ensuring the appropriateness of song lyrics is crucial. Using Retrieval-Augmented Generation (RAG) and PyTorch, this project introduces a structured approach to analyzing song lyrics by measuring their alignment with predefined appropriateness criteria. Through advanced natural language processing techniques with BERT, you’ll preprocess text, generate embeddings, and compute similarity scores to evaluate the semantic meaning of lyrics. This project demonstrates how to leverage embeddings to create actionable insights into content suitability, equipping you with practical AI tools for real-world applications.<br /><br /></div><h1>Why This Topic Is Important</h1><div>Understanding the appropriateness of content is crucial in scenarios like content moderation, education, and entertainment platforms. This project showcases how to combine AI-driven embeddings with practical evaluation techniques to assess content based on predefined themes. By completing this project, you will learn how to apply NLP techniques with PyTorch and RAG to measure semantic similarity and identify alignment with child-appropriate themes. The project emphasizes explainability and transparency by visualizing relationships through tools like t-SNE.<br /><br /></div><h1>A Look at the Project Ahead</h1><div>In this project, you will analyze song lyrics by aligning them with predefined questions about appropriateness using embeddings and similarity metrics. Learn how to preprocess data, generate embeddings with BERT, and visualize patterns using advanced dimensionality reduction techniques. By following this project, you will gain insights into embedding-based analysis, cosine similarity, and RAG-based workflows.</div><div>By the end of the project, you will:</div><ul><li>Understand how to preprocess text data for embedding generation using the BERT model.</li><li>Compute similarity between embeddings using dot product and cosine similarity to evaluate content alignment.</li><li>Visualize high-dimensional data using t-SNE to identify patterns and relationships between questions and song lyrics.</li><li>Create reusable functions to streamline embedding generation and similarity computation.</li></ul><div><br /></div><h1>What You'll Need</h1><div>To successfully complete this project, you’ll need:</div><ul><li>A foundational understanding of Python programming and libraries like PyTorch, pandas, and matplotlib.</li><li>Basic knowledge of natural language processing (NLP) and machine learning concepts.</li><li>A web browser to run your code and visualize results.</li></ul>
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RAG-Based Song Analysis for Appropriateness Using PyTorch

Learn to analyze song lyrics for appropriateness using RAG and PyTorch. Apply NLP techniques with BERT to generate embeddings, compute cosine similarity, and assess alignment with child-appropriate themes. This project covers preprocessing, similarity measurements, and t-SNE visualization. Ideal for data science and NLP enthusiasts, it offers hands-on experience in AI-driven text analysis. In just under 1 hour, master embedding-based analysis and create impactful insights.

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  <div>Gain the real-world experience of cybersecurity analysis employers look for with this hands-on capstone course.<br /><br />In this course, you’ll analyze real-life breach response case studies in areas such as phishing, vishing, point of sale (PoS) breaches, insider threats, AI-related breaches, and ransomware attacks. <br /><br />You’ll discover how attackers exploit vulnerabilities and how organizations respond and prevent future incidents.<br /><br />Additionally, you’ll gain expertise in incident response, digital forensics, penetration testing, and compliance through engaging case study analysis. You’ll complete several hands-on activities to assess response strategies and propose improvements. <br /><br />The course concludes with a final project, where you’ll select a real-world data breach, identify vulnerabilities, and assess the root cause, actions taken, and response strategies. Plus, a structured template will guide you to critically record observations and propose actionable improvements for breach management. This capstone course is a fantastic experience to talk about in interviews!<br /><br />If you’re keen to add practical cybersecurity experience to your resume, enroll today to complete this valuable capstone project in just six weeks!</div><h1>Course Syllabus</h1><div><strong>Module 1: Analyzing Case Study Layouts &amp; Phishing Case Studies <br />Lesson 1: Welcome to the Course</strong></div><ul><li>Video: Course Introduction </li><li>Reading: Course Overview  </li><li>Reading: Course Tracker/Syllabus</li><li>Plugin/Reading: Helpful Tips for Course Completion</li></ul><div><strong>Lesson 2: Analyzing Case Studies</strong></div><ul><li>Video: Exploring Case Studies </li><li>Video: How to Analyze Case Studies </li><li>Reading: Effective Layout for Analyzing Case Studies </li><li>Practice Quiz: Analyzing Case Studies </li></ul><div><strong>Lesson 3: Phishing Scams</strong></div><ul><li>Video: Overview of Phishing Scams </li><li>Activity: Can you Spot the Difference </li><li>Reading: Cisco Cyber Threat Trends Report </li><li>Video: Phishing Case Study: Google &amp; Facebook </li><li>Reading: Analyzing the Facebook and Google Case Study</li><li>Video: Vishing Case Study: South Korean Doctor</li><li>Activity: Identifying and Responding to Phishing Attacks</li><li>Practice Quiz: Phishing Scams </li></ul><div><strong>Module Summary and Assessment</strong> </div><ul><li>Reading: Summary and Highlights: Analyzing Case Study Layouts &amp; Phishing Case Studies </li><li>Graded Assignment: Graded Quiz: Analyzing Case Study Layouts &amp; Phishing Case Studies </li></ul><div><strong>Module 2: Analyzing PoS &amp; Insider Breach Case Studies <br />Lesson 1: Point of Sale (PoS) Breaches</strong></div><ul><li>Video: Overview of PoS Breaches </li><li>Video: PoS Malware and Skimming </li><li>Video: PoS Case Study: Target  </li><li>Reading: Cyber Kill Chain Analysis of the Target Breach</li><li>Video: PoS Case Study:Home Depot</li><li>Activity: Analyzing the Home Depot Case Study </li><li>Practice Quiz: Point of Sale (PoS) Breaches </li></ul><div><strong>Lesson 2: Insider Breaches</strong></div><ul><li>Video: Overview of Insider Breaches </li><li>Video: Insider Threat Detection</li><li>Reading: Mitigating Insider Threats</li><li>Video: Insider Breach Case Study: Vault 7 </li><li>Video: Insider Breach Case Study: Pegasus Airlines</li><li>Activity: Analyzing the Pegasus Airlines Case Study </li><li>Practice Quiz: Insider Breaches </li></ul><div><strong>Module Summary and Assessment</strong></div><ul><li>Reading: Summary and Highlights: Analyzing PoS &amp; Insider Breach Case Studies </li><li>Graded Quiz: Analyzing PoS &amp; Insider Breach Case Studies </li></ul><div><strong>Module 3:</strong> <strong>Analyzing AI-Related Breaches and Ransomware Case Studies </strong><br /><strong>Lesson 1:</strong> <strong>AI-Related Breaches</strong></div><ul><li>Video: Overview of AI-Related Breaches </li><li>Reading: The Emerging Impact of AI on Data Breaches  </li><li>Video: AI-Related Breach Case Study: Deepfake Conference Call</li><li>Activity : Analyzing the Deepfake Case Study </li><li>Video: Notable AI-Related Breaches </li><li>Practice Quiz: AI-Related Breaches</li></ul><div><strong>Lesson 2: Ransomware Breaches</strong></div><ul><li>Video: Overview of Ransomware Breaches </li><li>Reading: Emerging Trends in Ransomware </li><li>Video: Ransomware Case Study: City of Atlanta </li><li>Video: Ransomware Case Study: Colonial Pipeline </li><li>Activity: Navigating through a Ransomware Attack</li><li>Practice Quiz: Ransomware Breaches</li></ul><div><strong>Module Summary and Assessment</strong> </div><ul><li>Reading: Summary and Highlights: Analyzing AI-Related Breaches and Ransomware Case Studies </li><li>Graded Quiz: Analyzing AI-Related Breaches and Ransomware Case Studies </li></ul><div><strong>Module 4:</strong> <strong>Analyzing Incident Response and Digital Forensics Case Studies </strong><br /><strong>Lesson 1: Incident Response</strong></div><ul><li>Video: Overview of Incident Response </li><li>Video: Incident Response Case Study: Google Home</li><li>Video: Incident Response Case Study: Lightning Strikes </li><li>Activity: Analyzing the Lightning Strikes Case Study </li><li>Reading: When Incident Response Goes Wrong</li><li>Practice Quiz: Incident Response</li></ul><div><strong>Lesson 2: Digital Forensics</strong></div><ul><li>Video: Overview of Digital Forensics </li><li>Video: Digital Forensics Case Study: Bernard Madoff</li><li>Video: Digital Forensics Case Study: U.S. versus Ganias</li><li>Activity: Responding to a Digital Forensics Warrant</li><li>Reading: Digital Forensics Gone Wrong </li><li>Practice Quiz: Digital Forensics </li></ul><div><strong>Module Summary and Assessment</strong> </div><ul><li>Reading: Summary and Highlights: Analyzing Incident Response and Digital Forensics Case Studies </li><li>Graded Quiz: Analyzing Incident Response and Digital Forensics Case Studies</li></ul><div><strong>Module 5: Analyzing Penetration Testing and Compliance Case Studies </strong><br /><strong>Lesson 1: Penetration Testing</strong></div><ul><li>Video: Overview of Penetration Testing </li><li>Video: Penetration Testing Case Study: Equifax </li><li>Video: Penetration Testing Case Study: X-Force Red  </li><li>Activity: Analyzing the Penetration Testing Case Study: IBM X- Force Red</li><li>Reading: Penetration Testing Tales </li><li>Practice Quiz: Penetration Testing</li></ul><div><strong>Lesson 2: Compliance</strong></div><ul><li>Video: Overview of Compliance </li><li>Video: Compliance Case Study: Marriott </li><li>Video: Compliance Case Study: Equiniti</li><li>Mark: Analyzing the Equiniti Case Study </li><li>Reading: The Largest Cybersecurity Fines in History</li><li>Practice Quiz: Compliance </li></ul><div><strong>Module Summary and Assessment</strong> </div><ul><li>Reading: Summary and Highlights: Analyzing Penetration Testing and Compliance Case Studies </li><li>Graded Quiz: Analyzing Penetration Testing and Compliance Case Studies </li></ul><div><strong>Module 6:</strong> <strong>Final Project and Course Wrap-Up </strong><br /><strong>Lesson 1: Final Project Name</strong></div><ul><li>Reading: About the Course Project </li><li>Video: Introduction to Final Project: Analyzing a Data Breach  </li><li>Final Project: Analyzing a Data Breach  </li><li>Final Project: Submission and Evaluation </li></ul><div><strong>Lesson 2: Course Wrap Up</strong></div><ul><li>Video: Course Wrap-up </li><li>Reading: Congratulations and Next Steps</li><li>Reading: Team and Acknowledgments</li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>Prior knowledge of network security, threat hunting, incident response, penetration testing, and digital forensics is recommended.</div>
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Cybersecurity Case Studies and Capstone Project

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  <div>Immerse yourself in the future of storytelling with our guided project on <strong>AI-driven storytelling through Mixtral and text-to-speech integration</strong>. This project is designed for <strong>developers, educators, content creators, and tech enthusiasts</strong> who are eager to explore how technology can enhance narrative creation. In just <strong>30 minutes</strong>, you'll learn how to craft dynamic stories and bring them to life with lifelike audio. Whether you're working on <strong>audiobooks, educational materials, or interactive media</strong>, this project will help you create engaging and immersive audio experiences.<br /><br /><br /></div><h1><strong>What You'll Learn</strong></h1><div>By completing this project, you will:</div><ul><li>Discover how technology can enhance storytelling and its applications across different fields.</li><li>Use Mixtral to create compelling and dynamic narratives.</li><li>Convert written stories into high-quality audio using <strong><em>gTTS</em></strong> (<strong><em>Google Text-to-Speech</em></strong>).</li><li>Build immersive audio experiences for audiobooks, educational content, and interactive media.</li></ul><div><br /></div><h1><strong>What You'll Need</strong></h1><div>Before starting, make sure you have:</div><ul><li>Access to the <strong>IBM Skills Network Labs environment</strong>, which includes tools like Docker.</li><li>A current version of <strong>Chrome, Edge, Firefox, Internet Explorer, or Safari</strong> for the best experience.</li></ul>
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Stories to audio with Mixtral, gTTS (Google Text-to-Speech)

Discover AI-driven storytelling through Mixtral and text-to-speech (gTTS) integration. Learn to generate dynamic narratives and convert them into lifelike audio content. This guided project empowers developers, educators, content creators, and tech enthusiasts to create immersive storytelling experiences in just 30 minutes, perfect for audiobooks, educational resources, or interactive media.

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  <div>In this guided project, you will learn how to leverage AI-powered Natural Language Queries (NLQ) to interact with databases, like MySQL and MariaDB, using plain English. Through hands-on examples, you'll discover how to count records, describe table structures, join multiple tables, and perform grouping operations - all without writing SQL code. This lab demonstrates how AI can transform data analysis by making it more intuitive and accessible. By enabling users to simply ask questions in everyday language, teams can quickly gain valuable insights from their data without needing SQL expertise. In just 30 minutes, you'll set up a system that empowers business users and stakeholders to directly query databases, democratizing access to data-driven decision making.</div><div><br /><br />Natural language query (NLQ)       <strong>     </strong></div><div><strong>"How many employees are there" <br /></strong>AI-generated Structured Query Language (SQL) <strong><br />SELECT COUNT(*) FROM Employee </strong></div><div><strong>-------------------------------------------------------------------------------------------------------------------------<br /></strong>Natural language query (NLQ)            </div><div><strong>"How many Albums are there in the database?"<br /></strong>AI-generated Structured Query Language (SQL) <strong><br />SELECT COUNT(*) FROM Album          </strong></div><div><strong>-------------------------------------------------------------------------------------------------------------------------<br /></strong>Natural language query (NLQ)      <strong>      </strong></div><div><strong>"Can you left join table Artist and table Album by ArtistId? Please show me 5 Name and AlbumId in the joint table."<br /></strong>AI-generated Structured Query Language (SQL) <strong><br />SELECT Artist.Name, Album.AlbumId FROM Artist  LEFT JOIN Album ON Artist.ArtistId = Album.ArtistId LIMIT 5; <br />--------------------------------------------------------------------------------------------------------------------------------------<br /></strong>Natural language query (NLQ)        <strong>    </strong></div><div><strong>" Which country's customers spent the most by invoice?"<br /></strong>AI-generated Structured Query Language (SQL)<strong><br />SELECT Customer.Country, SUM(Invoice.Total) FROM Customer</strong></div><div><strong>INNER JOIN Invoice ON Customer.CustomerId = Invoice.CustomerId     <br />GROUP BY Customer.Country   <br />ORDER BY SUM(Invoice.Total) DESC LIMIT 1;   <br />--------------------------------------------------------------------------------------------------------------------------------------</strong><br /><br /><strong>**Natural Language Query (NLQ):** Describe the PlaylistTrack table</strong></div><div><strong>**Response:**</strong></div><div>
      <img src="https://author.skills.network/rails/active_storage/representations/redirect/eyJfcmFpbHMiOnsiZGF0YSI6MzkwNDIsInB1ciI6ImJsb2JfaWQifX0=--d96c5438dec330490a10017b280b250e29a96f33/eyJfcmFpbHMiOnsiZGF0YSI6eyJmb3JtYXQiOiJwbmciLCJsb2FkZXIiOnsibiI6LTF9LCJyZXNpemVfdG9fbGltaXQiOlsxMDI0LDc2OF19LCJwdXIiOiJ2YXJpYXRpb24ifX0=--d5cef5cc4423e679c464df97b6ff7a8fa3dec7ab/image.png" />

  
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</div><div><br /></div><div><strong>**Natural Language Query (NLQ):** How many employees are there</strong></div><div><strong>**Response:**</strong></div><div><br />
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</div><div><br /></div><div><strong>**Natural Language Query (NLQ):** Can you left join table Artist and table Album by ArtistId? Please show me 5 Name and AlbumId in the joint table.</strong></div><div><strong>**Response:**</strong></div><div>  </div><div>
      <img src="https://author.skills.network/rails/active_storage/representations/redirect/eyJfcmFpbHMiOnsiZGF0YSI6MzkwNDgsInB1ciI6ImJsb2JfaWQifX0=--7be036db4d72e3431481a14e6f969a3ec12b247a/eyJfcmFpbHMiOnsiZGF0YSI6eyJmb3JtYXQiOiJwbmciLCJsb2FkZXIiOnsibiI6LTF9LCJyZXNpemVfdG9fbGltaXQiOlsxMDI0LDc2OF19LCJwdXIiOiJ2YXJpYXRpb24ifX0=--d5cef5cc4423e679c464df97b6ff7a8fa3dec7ab/image.png" />

  
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<br /><br /><br /><br /></div><div><strong>What You'll Learn</strong></div><div>After completing this project, you will be able to:</div><div><br /></div><div>- <strong>**Integrate natural language processing**</strong>: Use AI tools to interpret natural language queries.</div><div>- <strong>**Execute SQL queries from natural language**</strong>: Translate natural language questions into SQL queries to fetch relevant data from the MySQL database.</div><div><br /></div><div><strong>What You'll Need</strong></div><div>Before starting this guided project, you should have:</div><div>- An understanding of natural language processing concepts is beneficial but not required.</div><div>- Access to a modern web browser such as Chrome, Edge, Firefox, Internet Explorer, or Safari.</div>
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Talk to MySQL using Natural Language Query (NLQ) with AI

Learn how to use AI-powered Natural Language Queries (NLQ) to interact with MySQL databases using plain English. You'll discover how to count, join, describe tables, and perform grouping operations - all without writing SQL code. This lab demonstrates how AI can transform data analysis by making it more intuitive and accessible. By enabling users to simply ask questions in everyday language, teams can quickly gain valuable insights from their data without needing SQL expertise.

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  <p>In order to have a successful professional life, you have to be able to communicate with the people you work with. You have to be able to communicate with co-workers in a way that establishes respect and influence. This short course is designed to teach you how to communicate with the people you work with in ways that shows confidence and gives them a sense of trust. By taking this course you will learn how to assert your influence and how you can use these skills to do things like resolve issues between people at work. </p>

<p>At every job, there is bound to be a conflict that will arise, and by taking this course you will gain the communication skills you need to resolve them. By learning the interpersonal skills that this course will equip you with, you will gain some foundational skills that will pave the way for your success. </p>

<p>This short course will provide you with the essential skills you need to be able to be able to successfully communicate and establish yourself with people in your professional life. The course is suitable for anyone who wants to learn, no matter how much experience you have.</p>

<p>This course is part of the People and Soft Skills for Professional and Personal Success Specialization from IBM.</p>
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Developing Interpersonal Skills

Learn to communicate with confidence and respect to establish yourself professionally with this short course from IBM.

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  <p>Knowing how to create a presentation is a powerful tool when it comes to communication. Presentations are a key part of every office or business related job and you need to have the skills to deliver a presentation that leaves an impact on viewers. This short course is designed to equip you with those exact skills that you will need in order to be successful in this field. Throughout this course you will learn how to create and deliver a presentation that will leave your audience well informed and impressed. Delivering a quality presentation is not just about reading off a slide, but also it is about body language, confidence and being ready to answer questions that your audience may throw at you.</p>

<p>By the end of this course, not only will you be able to create a skillful presentation, you will gain the confidence you need to deliver it with poise and leave an impressionable impact on your audience.</p>

<p>This course is part of the People and Soft Skills for Professional and Personal Success Specialization from IBM.</p>
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Present with Purpose: Create/Deliver Effective Presentations

Learn how to create and deliver powerful presentations that leave a lasting impression on your audience with this course.

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  <p>Collaboration is not just the act of working together. It goes beyond that. Through collaboration, you can accomplish much more than what you would if you worked alone. Having the skills to communicate with the people you are working with and being able to work as a team towards your goal are just some of the skills you will learn in this course to help you collaborate effectively in the workplace. In today's day and age, collaborating as a group does not just mean being able to see and communicate with each other in person, collaborations are done virtually all the time. This short course will teach you about some of the obstacles you may face while virtually collaborating with people and how to overcome these barriers with teamwork.</p>

<p>In order to collaborate successfully, you have to be able to recognize and apply different methods of communication. Collaborating involves working with other people, and when working with others you need to be able to understand their behaviors and how to effectively work together as a team in order to reach your goal.</p>

<p>This short course will help you develop the foundational soft skills to enhance the way you collaborate with your team, others in your company, business partners, and clients.</p>

<p>This course is part of the People and Soft Skills for Professional and Personal Success Specialization from IBM.</p>
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Collaborate Effectively for Professional Success

Gain essential skills to collaborate effectively in the workplace. Learn how to communicate with others, work as a team, and understand behaviors to reach your goal.

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  <p>This is the final course in the Full Stack Professional Certificate. It will test your knowledge and the skills you’ve acquired so far. This course contains the graded final examination covering content from nine of the eleven courses in the certificate. You will be assessed on topics such as core cloud computing concepts; languages such as HTML, CSS, JavaScript, and Python; frameworks such as Node.js and React; and backend technologies such as Docker, Kubernetes, OpenShift, SQL, Django, and Serverless.</p>
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Full Stack Software Developer Assessment

Test your mastery of cloud computing, web development, and backend technologies with this final course in the Full Stack Professional Certificate.

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  <h1>UI and UX Design</h1><div><br />UI and UX design is a thriving industry with numerous prospects for anyone with the right skills. This course is designed for those who are interested in starting a career in designing UI and UX experiences as well as developers who want to create web and front-end applications. It emphasizes the key concepts, tools, and methodologies used in front-end development. Web and mobile-accessible devices are transforming the world. Nowadays, everyone has a smartphone, tablet, smart TV, and/or laptop, which they use for communication and data searching. Companies are increasingly turning to front-end developers, mobile app developers, and UI/UX designers to assist them in producing user-friendly products. Therefore, it is crucial to provide users with a robust yet intuitive user interface and experience.<br /><br /></div><div><br />In this course, you will learn about the best practices of visual design development and identify the differences between well-designed and poor user interfaces. You will explore typography, readability, and colour theory in UI design.<br /><br /></div><div><br />You will gain insight into key methodologies in visual design like Responsive Web Design (RWD) and Progressive Web Design (PWD).<br /><br /></div><div><br />You will gain exposure to tools like Figma, a collaborative cloud-based app used for interface design. You will discover the steps to set up a Figma account and learn about its configuration. In addition, you will explore the various components, styles, libraries, and layout grids used in Figma that are essential for design.<br /><br /></div><div><br />Throughout this course, you will learn through videos, readings, activities, and quizzes designed to teach you the fundamentals of visual web and mobile design and development. You will gain a range of technical and practical knowledge and get insights and guidance from experts in the field.<br /><br /></div><div><br />The practical knowledge you will gain from the hands-on labs will further strengthen your base and enable you to perform better.<br /><br /></div><div><br />This is a beginner’s course, intended for learners with basic computer operating skills who have a fundamental knowledge of visual design development and are familiar with Figma.<br /><br /></div><div><br />To be successful in this course, you should have a basic understanding of User Interface (UI) and User Experience (UX) design as well as website wireframes, familiarity with the different kinds of layouts and tools used for visual design in various devices, and familiarity with the Figma application.<br /><br /></div>
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Designing User Experiences and User Interfaces (UX/UI)

Learn UI and UX design fundamentals, tools, and methodologies to create user-friendly products. Gain hands-on experience with Figma and other tools. Suitable for beginners.

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  <div>If you're ready to enter the world of Information Technology (IT), you need job-ready skills. This course provides the core hardware and operating system knowledge needed by anyone new to IT and computer hardware who wants to start a new career in technology, including IT Support, Networking, Cybersecurity, and Software Development. Learn about the software brain of a computer—its operating system software—and explore Microsoft Windows. Become knowledgeable of internal computer components, including motherboards, central processing units (CPUs), memory, hard drives, expansion slots, and others. Learn to identify interfaces, ports, connectors, and peripherals such as mice, keyboards, and others.<br /><br /></div><div><br />Explore workstation setup processes, commonly used operating system settings, screen capture commands, computer acquisition practices, and job-essential troubleshooting processes. You will also learn about mobile operating systems, Linux, and macOS, and complete a final project.<br /><br /></div><div><br />Throughout the course you will practice what you learn using interactive exercises and hands-on labs. This course is also part of the series to help you prepare for CompTIA ITF+ and A+ certification exams.<br /><br /></div>
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Introduction to Hardware and Operating Systems

Get ready to enter the IT world with job-ready skills. Learn Windows, computer components, troubleshooting, and more. Prepare for CompTIA ITF+ and A+ certification exams.

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  <div>Gain the daily work skills and knowledge you’ll need for IT Technical Support career success. This course, part of the IBM Technical Support Professional Certificate, is designed for beginners with no prior IT experience or formal degree.<br /><br /></div><div><br />Get an insider’s view into IT Support work. Learn about IT Support roles and levels, the support escalation matrix, ticketing systems, common support tools, and remote support software. Then, hear about career opportunities and career pathways from experienced, expert Information Technology professionals.<br /><br /></div><div><br />By the end of this course, you will be able to:<br /><br /></div><ul><li>Describe and develop a customer service and support mindset.</li><li>Define the levels of IT support and the escalation matrix.</li><li>Explore the features and benefits of ticketing systems.</li><li>Identify support tools and systems used in technical support.</li><li>Explore various tech support roles and responsibilities, including service level agreement responsibilities.</li><li>Recognize the career paths and progression in Technical Support.</li></ul><div><br />These skills will equip you to speak with prospective employers and ask insightful questions that demonstrate your knowledge of the day-to-day administrative role-related responsibilities.<br /><br /></div>
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Introduction to Technical Support

Gain the skills and knowledge needed to succeed in a career in IT Technical Support, with no prior experience or degree required. Learn about support roles, ticketing systems, remote support software, and more from experienced professionals. Gain a career-boosting edge with this

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  <div>In an increasingly data-centric world, the ability to derive meaningful insights from raw data is essential. The IBM Data Analyst Capstone Project gives you the opportunity to apply the skills and techniques learned throughout the IBM Data Analyst Professional Certificate. Working with actual datasets, you will carry out tasks commonly performed by professional data analysts, such as data collection from multiple sources, data wrangling, exploratory analysis, statistical analysis, data visualization, and creating interactive dashboards. Your final deliverable will include a comprehensive data analysis report, complete with an executive summary, detailed insights, and a conclusion for organizational stakeholders. <br /><br />Throughout the project, you will demonstrate your proficiency in tools such as Jupyter Notebooks, SQL, Relational Databases (RDBMS), and Business Intelligence (BI) tools like IBM Cognos Analytics. You will also apply Python libraries, including Pandas, Numpy, Scikit-learn, Scipy, Matplotlib, and Seaborn.  <br /><br />We recommend completing the previous courses in the Professional Certificate before starting this capstone project, as it integrates all key concepts and techniques into a single, real-world scenario.<br /><br /></div><h1>Learning Objectives</h1><ul><li>Apply techniques to gather and wrangle data from multiple sources.</li><li>Analyze data to identify patterns, trends, and insights through exploratory techniques.</li><li>Create visual representations of data using Python libraries to communicate findings effectively.</li><li>Construct interactive dashboards with BI tools to present and explore data dynamically.</li></ul><h1>Course Syllabus</h1><div><strong>Module 1: Welcome to the Course</strong></div><ul><li><strong>Lesson 0:</strong> Welcome</li><li><strong>Lesson 1:</strong> Collecting Data Using APIs</li><li><strong>Lesson 2:</strong> Collecting Data Using Web Scraping</li><li><strong>Lesson 3:</strong> Exploring Data</li></ul><div><strong>Module 2: Data Wrangling</strong></div><ul><li><strong>Lesson 1: </strong>Assignment Overview</li><li><strong>Lesson 2: </strong>Finding Duplicates</li><li><strong>Lesson 3: </strong>Removing Duplicates</li><li><strong>Lesson 4: </strong>Finding Missing Values</li><li><strong>Lesson 5: </strong>Imputing Missing Values</li><li><strong>Lesson 6: </strong>Normalizing Data</li></ul><div><strong>Module 3: Exploratory Data Analysis</strong></div><ul><li><strong>Lesson 1:</strong> Assignment Overview</li><li><strong>Lesson 2:</strong> Analyzing the Data Distribution</li><li><strong>Lesson 3:</strong> Handling Outliers</li><li><strong>Lesson 4:</strong> Correlation</li></ul><div><strong>Module 4: Data Visualization</strong></div><ul><li><strong>Lesson 1:</strong> Assignment Overview</li><li><strong>Lesson 2:</strong> Visualizing Distribution of Data</li><li><strong>Lesson 3:</strong> Visualizing Relationship</li><li><strong>Lesson 4:</strong> Visualizing Composition of Data</li><li><strong>Lesson 5:</strong> Visualizing Comparison of Data</li></ul><div><strong>Module 5: Building a Dashboard</strong></div><ul><li><strong>Lesson 1: </strong>Assignment Overview</li><li><strong>Lesson 2: </strong>Dashboards</li></ul><div><strong>Module 6: Final Assignment: Present Your Findings</strong></div><ul><li><strong>Lesson 1: </strong>How to Present Your Findings</li><li><strong>Lesson 2: </strong>Final Presentation</li><li><strong>Lesson 3: </strong>Course Wrap Up</li></ul><h1><strong>Course Prerequisites</strong></h1><div>This course requires you to be proficient in data analysis and have experience using SQL, Relational Databases, performing data collection, data wrangling, data analysis, &amp; data visualization with Python libraries, and using a BI tool like IBM Cognos Analytics or Google Looker. It is recommended that you complete all prior courses in the  <a href="https://www.coursera.org/professional-certificates/ibm-data-analyst#courses"><strong>IBM Data Analyst Professional Certificate</strong></a><strong> </strong>before starting this project course</div><ul><li>Introduction to Data Analytics</li><li>Excel Basics for Data Analysis</li><li>Data Visualization and Dashboards with Excel and Cognos</li><li>Python for Data Science, AI &amp; Development</li><li>Python Project for Data Science</li><li>Databases and SQL for Data Science with Python</li><li>Data Analysis with Python</li><li>Data Visualization with Python</li></ul>
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IBM Data Analyst Capstone Project

This course provides a capstone project to apply Data Analytics skills and techniques in a professional environment. You will work with real datasets to perform data collection, wrangling, analysis, visualization, and create interactive dashboards. Using tools like Jupyter Notebooks, SQL, IBM Cognos Analytics, and Python libraries (Pandas, Numpy, Scikit-learn, etc.), you will deliver a comprehensive analysis report for stakeholders. Completing prior courses in the certificate is recommended before starting this capstone project.

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  <div>Show your mastery of full stack and cloud native application development by completing this capstone project! In this course, you will apply your knowledge and skills to a real-life inspired challenge and use your expertise to develop a successful solution. The project provides you with an opportunity to solidify your full stack proficiency. As you design a dynamic user experience, you will work with GitHub actions to build, test, and deploy your application. You will develop frontend pages, add user administration, build actions for database operations, create backend services, make connections with cloud native APIs, and launch CI/CD pipelines. You will boost your capabilities with cloud native services, JavaScript, Django, JSON, IBM Code Engine, Python, and Kubernetes. Then you will devise a solution for managing the containerized deployment of your application.<br /><br /></div><div><br />This project is created for you to apply knowledge of relevant and in-demand skills such as: <br /><br /></div><ul><li>Designing applications and their architecture </li><li>Creating web frontends with static and dynamic pages using HTML, CSS, and JavaScript </li><li>Implementing user management and authentication </li><li>Creating and invoking RESTful microservices </li><li>Developing backend services and communicating with databases </li><li>Continuously Integrating and Deploying changes using CI/CD pipelines </li><li>Deploying the entire solution on Cloud using containers and serverless technologies </li></ul><div><br />When you complete this project, you will have a working cloud native application showpiece that will impress potential employers.<br /><br /></div><div><br />Before completing this project, it is highly recommended that you complete all the prior courses offered in the IBM Full Stack Software Developer Professional Certificate.<br /><br /></div>
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Full Stack Cloud Development Capstone Project

Master full stack and cloud native development with this project-based course! Design dynamic user experiences, deploy containers and serverless technologies, and create a successful solution.

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  <div>Showcase your skills in Data Engineering with this Python Project! This mini-course is intended to apply foundational Python skills by implementing different techniques to collect and work with data. You will assume the role of a Data Engineer and extract data from multiple file formats, transform it into specific datatypes, and then load it into a single source for analysis. Continue with the course and test your knowledge by implementing webscraping and extracting data with APIs all with the help of multiple hands-on labs. By the end of this project, you will have demonstrated your familiarity with important skills in Information Engineering and Extraction, Transformation and Loading (ETL), Jupyter Notebooks, and of course, Python Programming. <br /><br /></div><div><br />Upon completion of this course, you will have acquired the confidence to begin collecting large datasets, webscraping, using APIs, and performing ETL tasks, to hone valuable data management skills - all with the use of Python. <br /><br /></div><div><strong><br />PRE-REQUISITE:</strong> <em>Python for Data Science, AI and Development</em> course from IBM is a pre-requisite for this project course. Please ensure that before taking this course you have either completed the Python for Data Science, AI and Development course from IBM or have equivalent proficiency in working with Python and data. <br /><br /></div><div><strong><br />NOTE:</strong> This course is not intended to teach you Python and does not have too much instructional content. It is intended for you to apply prior Python knowledge.<br /><br /></div>
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Python Project for Data Engineering

Learn to apply your Python skills to collect, transform, and load data with this Data Engineering mini-course. Pre-requisite: Python for Data Science, AI and Development course.

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  <div>In this Machine Learning Capstone course, you will be using various Python-based machine learning libraries such as Pandas, scikit-learn, Tensorflow/Keras, to:<br /><br /></div><ul><li>build a course recommender system,</li><li>analyze course related datasets, calculate cosine similarity, and create a similarity matrix,</li><li>create recommendation systems by applying your knowledge of KNN, PCA, and non-negative matrix collaborative filtering,</li><li>build similarity-based recommender systems,</li><li>predict course ratings by training a neural network and constructing regression and classification models,</li><li>build a Streamlit app that displays your work, and</li><li>share your work then evaluate your peers.</li></ul>
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Machine Learning Capstone

Build a course recommender system using Python-based ML libraries, KNN, PCA, NNMF, Neural Networks, and Streamlit.

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  <div>This mini-course is intended to for you to demonstrate foundational Python skills for working with data. This course primarily involves completing a project in which you will assume the role of a Data Scientist or a Data Analyst and be provided with a real-world data set and a real-world inspired scenario to identify patterns and trends. You will perform specific data science and data analytics tasks such as extracting data, web scraping, visualizing data and creating a dashboard. This project will showcase your proficiency with Python and using libraries such as Pandas and Beautiful Soup within a Jupyter Notebook. Upon completion you will have an impressive project to add to your job portfolio.<br /><br /></div><div><strong><br />PRE-REQUISITE:</strong> <strong>Python for Data Science, AI and Development</strong> course from IBM is a pre-requisite for this project course. Please ensure that before taking this course you have either completed the Python for Data Science, AI and Development course from IBM or have equivalent proficiency in working with Python and data.<br /><br /></div><div><strong><br />NOTE:</strong> This course is not intended to teach you Python and does not have too much instructional content. It is intended for you to apply prior Python knowledge.<br /><br /></div>
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Python Project for Data Science

Learn data science and data analytics with Python! Complete a project to showcase your proficiency with extracting data, web scraping, visualizing data and creating a dashboard. Pre-requisite: Python for Data Science, AI and Development course from IBM.

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  <div>This course allows you to show what you’ve learned in the previous IT Technical Support professional certification courses and apply that knowledge to realistic situations. The IT Technical Support Capstone leads you through a series of technical support case studies that require hands-on work to resolve. You will practice analyzing user help requests and troubleshooting various issues. You’ll demonstrate your knowledge of hardware, software, networking, security, and cloud computing. You’ll also demonstrate your understanding of escalation, levels of support, ticketing systems, and other support tools and systems. And along the way, you’ll show what you’ve learned about the essential communication and customer service skills for effective technical support.<br /><br /></div><div><br />By the end of the course, you will better understand how to use your technical support skills in everyday professional settings. You’ll also be prepared to take the CompTIA ITF+ exam for certification.<br /><br /></div>
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Technical Support Case Studies and Capstone

This IT Technical Support Capstone course tests your knowledge of hardware, software, networking, security, and cloud computing, plus the customer service skills needed for effective technical support.

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  <div>Topics covered include:  <br />- collecting and importing data <br />- cleaning, preparing &amp; formatting data <br />- data frame manipulation <br />- summarizing data <br />- building machine learning regression models <br />- model refinement <br />- creating data pipelines <br /><br />You will learn how to import data from multiple sources, clean and wrangle data, perform exploratory data analysis (EDA), and create meaningful data visualizations. You will then predict future trends from data by developing linear, multiple, polynomial regression models &amp; pipelines and learn how to evaluate them.  <br /><br />In addition to video lectures you will learn and practice using hands-on labs and projects. You will work with several open source Python libraries, including Pandas and Numpy to load, manipulate, analyze, and visualize cool datasets. You will also work with scipy and scikit-learn, to build machine learning models and make predictions.  <br /><br />If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge.</div>
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Data Anlysis with Python

Analyzing data with Python is an essential skill for Data Scientists and Data Analysts. This course will take you from the basics of data analysis with Python to building and evaluating data models.

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  <div>The course will start with Pytorch's  tensors and Automatic differentiation package. Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression. Followed by  Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers. Then Convolutional Neural Networks and Transfer learning will be covered. Finally, several other Deep learning methods will be covered.<br /><br /></div><div>Learning Outcomes:<br />After completing this course, learners will be able to:<br />• explain and apply their knowledge of Deep Neural Networks and related machine learning methods<br />• know how to use Python libraries such as PyTorch  for Deep Learning applications <br />• build Deep Neural Networks using PyTorch</div>
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Deep Neural Networks with PyTorch

The course will teach you how to develop deep learning models using Pytorch.

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  <div>Work with Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. You will understand what each tool is used for, what programming languages they can execute, their features and limitations.  <br /><br />This course gives plenty of hands-on experience in order to develop skills for working with these Data Science Tools. With the tools hosted in the cloud on Skills Network Labs, you will be able to test each tool and follow instructions to run simple code in Python, R, or Scala.  <br /><br />Towards the end the course, you will create a final project with a Jupyter Notebook. You will demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.</div>
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Tools for Data Science

In order to be successful in Data Science, you need to be skilled with using tools that Data Science professionals employ as part of their jobs. This course teaches you about the popular tools in Data Science and how to use them. You will become familiar with the Data Scientist’s tool kit which includes: Libraries & Packages, Data Sets, Machine Learning Models, Kernels, as well as the various Open source, commercial, Big Data and Cloud-based tools.

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