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  <div>In this course, you will explore the impact of generative artificial intelligence (AI) on society, the workforce, organizations, and the environment.<br /><br /></div><div>This course is suitable for anyone interested in learning about the ethical, economic, and social implications of generative AI and how generative AI can be used responsibly. It will benefit professionals, executives, policymakers, and students.<br /><br /></div><div>In this course, you will learn about the ethical concerns of generative AI, including data privacy, biases, copyright infringement, and hallucination. You will identify the misuses related to generative AI, including deepfakes.<br /><br /></div><div>Further, in the course, you will examine the considerations for the responsible use of generative AI. You will explore the broader implications of generative AI on transparency, accountability, privacy, and safety. Finally, you will learn about the socioeconomic impacts of generative AI.<br /><br /></div><div>The examples and cases included in the course help to realize the considerations for generative AI in real-life scenarios. You will hear from practitioners about the realities, limitations, and ethical considerations of generative AI.<br /><br />After completing this course, you will be able to:</div><ul><li>Describe the limitations of generative AI and the related concerns.</li><li>Discuss the ethical issues, concerns, and misuses associated with generative AI.</li><li>Explain the considerations for the responsible use of generative AI.</li><li>Discuss the economic and social impact of generative AI.</li><li>Explain the impact of generative AI on jobs and the workforce.</li></ul><h1>Course Syllabus</h1><div><strong>Welcome</strong></div><ul><li>Video: Course Introduction</li><li>Reading: Course Overview</li><li>Reading: Program/Specialization Overview</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: Limitations and Ethical Issues of Generative AI</strong></div><ul><li>Reading: Module Introduction and Learning Objectives</li><li>Video: Limitations of Generative AI</li><li>Video: Issues and Concerns About Generative AI</li><li>Video: Hallucinations of Text and Image-Generating LLMs</li><li>Video: Hallucinations of Code-Generating LLMs</li><li>Lab: Hallucinations of LLMs</li><li>Video: AI Portraits and Deepfakes</li><li>Lab: Creating AI Portraits</li><li>Video: Enhancing LLM Accuracy with RAG</li><li>Video: Legal Issues and Implications of Generative AI</li><li>Discussion Prompt: Ethical Issues about Generative AI</li><li>Expert Viewpoints Video: Issues and Ethical Concerns Associated with Generative AI</li><li>Expert Viewpoints Video: Limitations and Challenges of Generative AI</li><li>Expert Viewpoints Video: Regulating Challenges Posed by Generative AI</li><li>Reading: Module Summary</li><li>Practice Quiz: Limitations, Concerns, and Issues of Generative AI</li><li>Graded Quiz: Limitations and Ethical Issues of Generative AI</li></ul><div><strong>Module 2: Social and Economic Impact and Responsible Generative AI</strong></div><ul><li>Reading: Module Introduction and Learning Objectives</li><li>Video: Considerations for Responsible Generative AI</li><li>Video: Implementing Responsible Generative AI Across Domains</li><li>Lab: Ethical Considerations for Generative AI in Different Domains</li><li>Video: AI Ethics: Perspective of Key Players</li><li>Practice Quiz: Considerations for Responsible AI</li><li>Video: Economic Implications of Generative AI</li><li>Video: Social Implications of Generative AI</li><li>Video: A Reimagined Workforce with Generative AI</li><li>Discussion Prompt: Generative AI's Influence on Mental Health</li><li>Expert Viewpoints Video: AI Ethics Understanding the Focus of Key Players</li><li>Expert Viewpoints Video: What are the Key Considerations for the Responsible Use of Generative AI</li><li>Expert Viewpoints Video: Impact of Generative AI on Society</li><li>Expert Viewpoints Video: Integrating Generative AI into Organizational Culture</li><li>Reading: Module Summary</li><li>Practice Quiz: Social and Economic Impact</li><li>Graded Quiz: Social and Economic Impact and Responsible Generative AI</li></ul><div><strong>Module 3: Course Quiz and Final Project</strong></div><ul><li>Reading: Module Introduction and Learning Objectives</li><li>Glossary: Generative AI: Impact, Considerations, and Ethical Issues</li><li>Reading: Implementing Responsible Generative AI</li><li>Final Project: Preparing Your Mind for Generative AI</li><li>Final Exam: Generative AI: Impact, Considerations, and Ethical Issues</li></ul><div><strong>Course Wrap-Up</strong></div><ul><li>Reading: Congratulations and Next Steps</li><li>Thanks from the Course Team</li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>This course is designed for anyone interested in the subject of generative AI. No prior knowledge or experience of programming is required.  <br /><br />**Note:** This platform works best with current versions of Chrome, Edge, Firefox or Safari.</div>
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Impact, Ethics, and Issues with Generative AI

Dive into the world of Generative AI with a focus on different ethical factors related to data privacy, security, the workforce, and the environment. Learn about the socioeconomic impacts of generative AI and related issues such as job and copyright implications. You'll also explore the ethical concerns related to generative AI and examine how to use it responsibly.

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  <h1>About this course</h1><div>Begin your cloud computing mastery with this easy-to-follow, self-paced course. Gain the essential knowledge needed to understand cloud computing from a business perspective and as a hands-on cloud practitioner.<br /><br />This course immerses you in the world of notable cloud service providers, including AWS, Google, IBM, and Microsoft. Explore their offered services and examine real-world applications through industry-specific case studies. You'll also dive into cloud adoption and its connection with technologies such as blockchain, analytics, and AI.<br /><br />Discover the diverse cloud service models—Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS)—and deployment models—public, private, and hybrid. You'll grasp the core elements of cloud infrastructure, including VMs, networking, storage types, and CDNs. You’ll learn about emerging trends like Hybrid Multicloud, Microservices, Serverless, DevOps, Cloud Native, and Application Modernization. Learn the basics of cloud security, monitoring, and understand various roles in the cloud industry.<br /><br />Throughout the course, you’ll hear from subject matter experts who will share their insights into Cloud technology and work as a Cloud Practitioner.<br /><br />You'll gain hands-on experience, having deployed an application on IBM Cloud—a shareable project that makes for an impressive employment portfolio addition!<br /><br />Whether you're an executive, manager, student, or professional seeking familiarity with cloud computing terminology and concepts, or you aim for a solid grounding in cloud computing for a career as a cloud engineer, full stack developer, DevOps engineer, service reliability engineer, or cybersecurity analyst, this course is your starting point.<br /><br /><strong>What you'll learn<br /><br /></strong>After completing this course, a learner will be able to:</div><ul><li>Define cloud computing and explain essential characteristics, history, the business case for Cloud, and the emerging technologies enabled by Cloud.</li><li>Describe Cloud service models, including IaaS, PaaS, SaaS</li><li>Describe Public, Private, and Hybrid Cloud deployment models.</li><li>Explain Cloud infrastructure components.</li><li>Explain emerging cloud-related trends, including Hybrid, Multicloud, Microservices, Serverless, Cloud Native, DevOps, and Application Modernization.</li><li>List and describe services of popular cloud platforms, including AWS, Microsoft Azure, Google Cloud, IBM Cloud, Alibaba Cloud, and others.</li></ul><h1>Course syllabus</h1><div><strong>Module 1: Overview of Cloud Computing</strong></div><ul><li>Introduction and Learning Objectives</li><li>Definition and essential characteristics of cloud computing</li><li>Expert Viewpoints: Definition and Essential Characteristics of Cloud Computing</li><li>History and Evolution of Cloud Computing</li><li>Key Considerations for Cloud Computing</li><li>Key Cloud Service Providers and Their Services</li><li>Cloud Adoption - No Longer a Choice</li><li>Expert Viewpoints: Cloud Adoption Benefits and Use Cases</li><li>Cloud Adoption - Some Case Studies</li><li>Internet of Things on the Cloud</li><li>Artificial Intelligence on the Cloud</li><li>Blockchain and Analytics on the Cloud</li></ul><div><strong>Module 2: Cloud Computing Models</strong></div><ul><li>Introduction and Learning Objectives</li><li>Overview of Cloud Service Models</li><li>Infrastructure-as-a-Service</li><li>Platform-as-a-Service</li><li>Software-as-a-Service</li><li>Public Cloud</li><li>Private Cloud</li><li>Hybrid Cloud</li><li>Expert Viewpoints: Cloud Computing Service and Deployment Models</li><li>Community Cloud</li></ul><div><strong>Module 3: Components of Cloud Computing</strong></div><ul><li>Introduction and Learning Objectives</li><li>Overview of Cloud Infrastructure</li><li>Virtualization and Virtual Machines Explained</li><li>Types of Virtual Machines</li><li>Bare Metal Servers</li><li>Secure Cloud Networking</li><li>Containers</li><li>Expert Viewpoints: Components of Cloud Computing</li><li>Basics of Cloud Storage</li><li>File Storage</li><li>Block Storage</li><li>Object Storage Overview</li><li>Object Storage - Tiers and APIs</li><li>Content Delivery Networks</li><li>Expert Viewpoints: Cloud Storage</li></ul><div><strong>Module 4 - Emergent Trends and Practices</strong></div><ul><li>Introduction and Learning Objectives</li><li>Hybrid Multi-Cloud</li><li>Microservices</li><li>Serverless Computing</li><li>Cloud Native Applications</li><li>DevOps on the Cloud</li><li>Application Modernization</li><li>Expert Viewpoints: Cloud Native and Emergent Cloud Trends</li></ul><div><strong>Module 5: Cloud Security and Monitoring, Case Studies, and Jobs</strong></div><ul><li>Introduction and Learning Objectives</li><li>What is Cloud Security - Part 1</li><li>What is Cloud Security - Part 2</li><li>Policies and Principles of Access Management</li><li>Identity and Access Management</li><li>Cloud encryption</li><li>Cloud Monitoring Basics and Benefits</li><li>Cloud Monitoring Benefits</li><li>Case Studies in Different Industry Verticals</li><li>Career Opportunities and Job Roles in Cloud Computing</li><li>Expert Viewpoints: Job Market for Cloud Computing</li><li>Final Project: Deploy an application on the cloud</li><li>HONORS Assignment: Architect a Cloud-based solution</li><li>Final Exam</li></ul><h1>Recommended skills prior to taking the course</h1><div>Basic computer literacy. No prior knowledge of Cloud Computing or Computer Programming.</div>
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Introduction to Cloud Computing

This course introduces you to the core concepts of cloud computing. You will gain the foundational knowledge required for understanding cloud computing from both business and practitioner perspectives. You will also learn about cloud adoption and emerging technologies, cloud computing service and deployment models, different types of cloud storage, and emergent cloud trends.

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  <div>In this project, you will apply your knowledge of front-end and back-end development to build and deploy a full-stack real-world web application on the cloud.<br /><br />Your project will showcase your understanding of the user experience and user interface design process, and your skills and technologies with Django, Python, Node.js, and Containers.<br /><br />After completing this project, you will have gained hands-on practical experience with a real-world project and be able to demonstrate your proficiency in front-end and back-end skills to potential employers.</div><h1>Learning Objectives:</h1><div>By the end of this course, you will be able to:</div><ul><li>Articulate the attributes of multiple cloud services, including Watson AI, Cloudant, and Cloud Object Storage.</li><li>Sharpen your skillset using JavaScript, React.js, Python, Node.js, Express and more.</li><li>Build a real-world web application using front-end and back-end technology.</li><li>Deploy your Django full-stack web application on the cloud</li></ul><div><br /></div><h1>Prerequisites:</h1><div>Complete all previous courses within the Full-Stack Cloud Developer Professional Certificate </div><div><br /></div><h1>Course Syllabus:</h1><div><strong>Module 1: Application – Static Pages </strong></div><div><strong>Module 2: Application – User Management </strong></div><div><strong>Module 3: Back End Services </strong></div><div><strong>Module 4: Application – Dynamic Pages (React.js) </strong></div><div><strong>Module 5: CI/CD, Containerize &amp; Deploy to Kubernetes </strong></div><div><strong>Module 6: Share Your Project </strong></div>
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Full Stack Software Developer Capstone

While completing this project, you will apply your front-end and back-end development knowledge to build and deploy the application on the cloud. 

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  <div>Collaboration means so much more than simply working together; it goes beyond that, and this course teaches you how. Through collaboration, you can achieve so much more than you could if you had worked alone. Some of the essential skills taught in this course include effectively communicating with the people you work with, being a team player, and working as a team towards a shared goal.<br /><br /></div><div>In today’s day and age and in the modern workspace, group collaboration is not limited to just the workplace. Collaboration is no longer just in person, as collaboration is now done virtually all the time! This course will teach you how to collaborate both virtually and in person.<br /><br /></div><div>This short course not only teaches you about some of the drawbacks and obstacles of different styles of communication, it also teaches you how to recognize and apply proper and different methods of communication. You will learn to overcome obstacles that you may face when collaborating and strive for professional success.<br /><br /></div><div>Effective collaboration requires the involvement of diverse individuals and teamwork. When working with others, you need to be able to read their body language and understand their behaviors. In this course, you will learn how to do this and work successfully together.<br /><br /></div><div>By the end of this self-paced, short course, you will have developed the skills you need to collaborate with a team, others in your company, business partners, and clients.<br /><br /></div><div>This course is part of the People and Power Skills Professional Certificate from IBM, which consists of a series of short courses to help you develop essential soft skills for success in the workplace.<br /><br /><strong>What you'll learn</strong></div><ul><li>Identify different collaborative barriers as well as the specific behaviors that enhance or get in the way of collaboration</li><li>Go through the four stages of team development using different collaborative techniques</li><li>Improve teamwork and collaboration in your professional environment</li><li>Effectively lead and participate in virtual meetings, an increasingly popular form of collaboration</li></ul><div><strong>The course is composed of the following lessons:<br /></strong><br /></div><div>- Collaborate Effectively: An Overview</div><div>- About the Course</div><div>- What is collaboration, and why is it so important?</div><div>- How can you enhance your collaboration with colleagues?</div><div>- How can you help your team collaborate?</div><div>- What are the challenges of virtual collaboration, and how can you tackle them?</div><div>- What tools can you use for virtual collaboration?</div><div>- How can you make virtual meetings effective?</div><div>- Course-end Assessment<br /><br /></div><div><strong>Prerequisites<br /></strong><br />This course does not require any previous work experience and is great if you are just beginning your career or if you are looking for a skill refresher.<br /><br /></div>
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Soft Skills: Collaborate Effectively

Learn the fundamentals of collaborating and communicating with others in a professional environment in order to achieve professional success.

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  <div><strong>About this course</strong></div><div>When submitting work in a professional setting, it is crucial to meet both the quality and time expectations set by your employer. This short course will teach you about how to deliver quality work with consistency and efficiency.<br /><br /></div><div>Effective research skills play a vital role in ensuring the delivery of quality work. By taking this short, self-paced course, you will learn exactly how to do that. You will also learn a variety of other skills that are essential, such as a five-step process that will teach you how to create and deliver quality work.<br /><br /></div><div>The skills taught in this course are fundamental skills that are important in any job you may have, especially office or business-related jobs. The content provided in this course applies to your everyday work life and will be a stepping stone in your path to success.<br /><br /></div><div>This course is part of the People and Power Skills Professional Certificate from IBM.<br /><br /></div><div><strong>What you'll learn</strong></div><ul><li>Deliver work consistently and on time</li><li>Apply a five-step process that will help you deliver high-quality work</li><li>Create and deliver work that helps solve problems and reduces effort for the person or group requesting it</li><li>Become a trusted advisor to clients and colleagues by working with agility while applying the quality work process</li></ul><div><strong>Prerequisites<br /></strong><br /></div><div>No prior experience is necessary! This course is suitable for those taking it as a career starter or even just a skill refresher.<br /><br /></div><div><strong>Curriculum</strong></div><div><br /></div><div>The course is composed of the following lessons:<br /><br /></div><div>- Delivering Quality Work with Agility: An overview</div><div>- About the Course</div><div>- Topic 1: What do I need to know?</div><div>- Topic 2: How do I prepare?</div><div>- Topic 3: How do I do my research?</div><div>- Topic 4: How do I decide what to do?</div><div>- Topic 5: How do I do it?</div><div>- Topic 6: How do I deliver it?</div><div>- Topic 7: Who benefits when I deliver quality work?</div><div>- Course-end Assessment</div>
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Soft Skills: Deliver Quality Work with Agility

Understand the importance of being able to create and deliver high-quality work efficiently, as it is a crucial soft skill for enhancing professional success.

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  <div><strong>About this course</strong></div><div>The most essential skills needed to be successful in your professional life are being able to effectively communicate and interact with the people you work with. These are called interpersonal skills. Being able to talk with your co-workers in a way that establishes respect and eminence is an incredibly important ability to have in the workplace.<br /><br /></div><div>This short, self-paced course will teach you the skills you need in order to communicate and interact with people in a way that shows confidence and gives them a sense of trust. In this course, you will learn skills such as asserting your influence, different communication styles, listening skills, and more!<br /><br /></div><div>No matter where you work, conflicts are something that will naturally arise, and after taking this course, you will be able to recognize and apply the interpersonal skills you need in order to resolve them. The skills offered in this course are foundational soft skills to have and will take you one step closer to professional success.<br /><br /></div><div>This short course can be taken by anyone who wants to learn, no matter how much experience they have. Whether you are taking this course as a career starter or skill refresher, this course is for everyone!<br /><br /></div><div>This course is part of the People and Power Skills Professional Certificate from IBM, which consists of a series of short courses to help you develop essential soft skills for success in the workplace.<br /><br /></div><div><strong>What you'll learn</strong></div><ul><li>Analyze different communication styles as well as their strengths and weaknesses, and when they are appropriate to use</li><li>Build and maintain trust with your co-workers and increase your influence and eminence in the workplace</li><li>Identify ways to approach and mediate workplace conflicts</li><li>Demonstrate active listening skills to enhance your understanding of workplace issues</li></ul><div><strong>Prerequisites<br /></strong><br /></div><div>This course is suitable for anyone, regardless of previous experience. This course is also perfect for people just starting or in their early careers.<br /><br /></div><div><strong>Curriculum</strong></div><div><br /></div><div>The course is composed of the following lessons:</div><div>- Interpersonal skills: An overview</div><div>- About the course</div><div>- Topic 1: What is my communication style?</div><div>- Topic 2: How can I become a better communicator?</div><div>- Topic 3: How can I behave assertively?</div><div>- Topic 4: How can I increase my influence?</div><div>- Topic 5: How should I approach workplace conflict?</div><div>- Topic 6: How can I resolve workplace conflict?</div><div>- Course-end Assessment</div>
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Soft Skills: Develop Interpersonal Skills

Learn key interpersonal skills: how to effectively communicate and interact with others while establishing your presence in a professional workspace!

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  <div><strong>About this course</strong></div><div>This short course is the final assessment for the People and Power Skills Professional Certificate. It serves as a culmination of the knowledge and abilities gained throughout the entire program. By completing this assessment, you will demonstrate your proficiency in key areas essential for professional success.<br /><br /></div><div>The People and Power Skills Professional Certificate is a comprehensive program designed to equip you with the essential soft skills necessary to excel in today's professional landscape. This certificate program focuses on developing crucial soft skills that are highly sought after by employers across industries.<br /><br /></div><div>Before taking this assessment, please ensure that you have completed the previous 5 courses offered in the <a href="https://www.edx.org/professional-certificate/ibm-people-and-power-skills">People and Power Skills Professional Certificate</a>. These include:</div><ol><li><a href="https://www.edx.org/course/soft-skills-present-with-purpose">Soft Skills: Present with Purpose</a></li><li><a href="https://www.edx.org/course/soft-skills-solve-problems-w-creative-critical-thinking">Soft Skills: Solve Problems w/ Creative &amp; Critical Thinking</a></li><li><a href="https://www.edx.org/course/soft-skills-develop-interpersonal-skills">Soft Skills: Develop Interpersonal Skills</a></li><li><a href="https://www.edx.org/course/soft-skills-deliver-quality-work-with-agility">Soft Skills: Deliver Quality Work with Agility</a></li><li><a href="https://www.edx.org/course/people-skills-collaborate-effectively">Soft Skills: Collaborate Effectively</a></li></ol><div>In this assessment, you will be presented with many real-life inspired scenarios and be questioned on what to do if you were to encounter them. You will be taking your knowledge of previous courses in this certificate and applying them to these scenarios.<br /><br /></div><div>Upon completion of this course, you will receive your Professional Certificate in People and Power Skills if you are in the verified track for the entire program.<br /><br /></div><div><strong>What you'll learn</strong></div><ul><li>Analyze and solve problems with the use of creative and critical thinking</li><li>Show a working knowledge of interpersonal skills by identifying different communication styles and conflict responses</li><li>Exhibit your skills in creating and delivering presentations that leave a lasting impression</li><li>Showcase your ability to create and deliver quality work in a professional environment</li><li>Apply your knowledge of techniques to collaborate effectively</li></ul><div><strong>Prerequisites<br /></strong><br /></div><div>Before taking this course, please ensure you have completed all of the other 5 courses in the IBM People and Power Skills Professional Certificate.<br /><br /></div><div><strong>Curriculum</strong></div><div><br /></div><div>The course is composed of the following lessons:<br /><br /></div><div>- People and Power Skills Assessment: An Overview</div><div>- About the Course</div><div>- Check your Knowledge</div><div>- Continue your Learning</div>
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Soft Skills: People and Power Skills Assessment

This course contains the final assessment where you will apply your knowledge of the courses in the People and Power Skills Professional Certificate!

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  <div><strong>About this course</strong></div><div>Being able to create a presentation is an important skill in a professional work environment, especially when you are working in an office or business-related setting. Knowing how to create a presentation that leaves a lasting impact is an essential skill to have.<br /><br /></div><div>This short course was created to equip you with the skills to do exactly that. In this course, you will learn to create and deliver a presentation that keeps your audience well-informed, engaged, and leaves them impressed and with a lasting impression. Delivering a good presentation is not only about reading memorized lines off a slide; it is also a matter of confidence, body language, managing nerves, and being prepared for any questions that may be thrown your way by your audience.<br /><br /></div><div>By the end of this short course, you will know how to create and deliver an effective presentation with poise and confidence, which will contribute to your workplace presence and help you achieve professional success.<br /><br /></div><div>This course is part of the People and Power Skills Professional Certificate from IBM, which consists of a series of short courses to help you develop essential soft skills for success in the workplace.<br /><br /></div><div><strong>What you'll learn</strong></div><ul><li>Present and deliver presentations with confidence</li><li>Create powerful and engaging presentations that keep your audience's attention</li><li>Use storytelling, props, and discussion techniques to enhance your presentation</li><li>Learn how to keep calm and be able to deal with any unexpected problems that may arise during your presentation</li></ul><div><strong>Prerequisites<br /></strong><br /></div><div>No prior work experience required! This is a great course to take as both a career starter or a skill refresher.<br /><br /></div><div><strong>Curriculum</strong></div><div><br /></div><div>The course is composed of the following lessons:</div><div>- Present with Purpose: An overview</div><div>- About the Course</div><div>- Present with Purpose: An Introduction</div><div>- Topic 1: How do I focus on my objectives?</div><div>- Topic 2: How do I identify my audience?</div><div>- Topic 3: How do I build my structure?</div><div>- Topic 4: How do I design my deck?</div><div>- Topic 5: How do I present more effectively?</div><div>- Topic 6: How do I manage my nerves?</div><div>- Topic 7: How do I deal with the unexpected?</div><div>- Course-end Assessment</div>
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Soft Skills: Present with Purpose

This short course will teach you how to create and deliver presentations that leave an effective and lasting impact on your audience!

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  <div>In this course, you will discover the transformative impact of generative AI on businesses and professionals and how generative is elevating opportunities for businesses and professionals in different industries.<br /><br />This course is designed for anyone interested in learning about business and career opportunities through generative AI. It will benefit a diverse audience, including professionals in various domains, executives, managers, startup founders, and students.<br /><br />In this course, you will discover the influence of generative AI on existing businesses and opportunities for new businesses. You will learn how to make a business ready for generative AI adoption. You will identify the impact of generative AI across various industries.<br /><br />Next, you will discover the potential career opportunities in the generative AI sector. You will learn how generative AI can impact and enhance the existing functions, skills, and job roles in different sectors.<br /><br />In this course, you will hear from practitioners about the current trends and future perspectives of generative AI, exploring its potential to reshape industries and redefine professions. The course includes labs and projects, providing you with an opportunity to demonstrate your skills and understanding of generative AI.</div><div><br /></div><h1>Course Syllabus</h1><div><strong> Generative AI in Business: Trends, Ideas, and Implementation</strong></div><ul><li>Current Trends and Future Perspectives of Generative AI</li><li>Elevating Businesses through Generative AI</li><li>An Ecosystem of Generative AI Providers </li><li>RAG: Opportunity and Potential Applications for Business</li><li>Impact of Generative AI in Different Industries</li></ul><div><strong>Generative AI: Impact and Opportunities for Career Growth</strong></div><ul><li>Career Opportunities in Generative AI</li><li>Enhancing Your Career with Generative AI</li><li>Generative AI for Content Creators </li><li>Generative AI Applications for Content Creators</li><li>Generative AI for IT Professionals</li><li>Generative AI Applications for IT Professionals</li><li>Custom GPTs: Personalized AI</li><li>Creating Custom GPTs</li><li>Generative AI for Executives and Managers</li></ul><div><strong>Course Quiz, Project, and Wrap-up<br /></strong><br /></div><h1><strong>What you'll learn</strong></h1><ul><li>Explain the transformative impact of generative AI on the business and industry landscape.</li><li>Identify key indicators of readiness for integrating generative AI into business.</li><li>Explore potential career opportunities in generative AI.</li><li>Analyze how generative AI can enhance existing careers in diverse fields.</li><li>Apply generative AI to boost your productivity at work.</li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>This course is designed for anyone interested in the subject of generative AI. No prior knowledge or experience of programming is required.  <br /><br />**Note:** This platform works best with current versions of Chrome, Edge, Firefox or Safari.</div>
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Elevating Businesses and Careers with Generative AI

Unlock the power of generative AI for your career and business success! Dive into this comprehensive course designed for professionals and enthusiasts eager to explore the dynamic world of generative AI. Learn how to leverage generative AI for existing businesses, identify new opportunities, and adapt your skills for tomorrow's job market. Gain insights from industry experts, hands-on experience, and emerge ready to thrive in the AI-driven future. Enroll now to stay ahead in the ever-evolving landscape of technology and innovation.

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  <div>In this Capstone you’ll demonstrate your ability to perform like a Data Engineer. Your mission is to design, implement, and manage a complete data and analytics platform consisting of relational and non-relational databases, data warehouses, data pipelines, big data processing engines, and Business Intelligence (BI) tools.<br /><br /></div><div>This Capstone project will require that you apply and sharpen the skills and knowledge you developed in the various courses in the IBM Data Engineering Professional Certificate and utilize multiple tools and technologies to design databases, collect data from multiple sources, extract, transform and load data into a data warehouse, and utilize a cloud-based BI tool to create analytic reports and visualizations. You will also implement predictive analytics and machine learning models using big data tools and techniques.<br /><br /></div><div>This capstone requires significant amount of hands-on lab effort throughout the course. You’ll exhibit your knowledge and proficiency working with Python, Bash scripts, SQL, NoSQL, RDBMSes, ETL, MySQL, PostgreSQL, Db2, MongoDB, Apache Airflow, Apache Spark, and Cognos Analytics.<br /><br /></div><div>Upon successfully completing this Capstone, you should have the confidence and portfolio to take on real-world data engineering projects and showcase your abilities to perform as an entry-level data engineer.</div><div><br /></div><h1>What you will learn: </h1><div>After completing this course, you will be able to: </div><ul><li>Build a complete data and analytics platform.</li><li>Setup, manage, and query relational and NoSQL databases.</li><li>Create data pipelines and ETL processes using Apache Airflow.</li><li>Design and populate a star/snowflake schema data warehouse and query it using SQL.</li><li>Analyze warehouse data using the Business Intelligence (BI) tool Cognos Analytics to create reports and dashboards.</li><li>Deploy a big data machine learning model using Apache Spark</li></ul><h1>Course Syllabus</h1><div><strong>Module 1 - Data Platform Architecture and OLTP Database</strong></div><ul><li>Assignment Overview</li><li>OLTP Database</li><li>OLTP Database Requirements and Design</li><li>Hands-on Lab: OLTP Database</li><li>Checklist: OLTP Database</li><li>Graded Quiz: OLTP Database</li></ul><div><strong>Module 2 - Querying Data in NoSQL Databases</strong></div><ul><li>Module Introduction and Learning Objectives</li><li>Assignment Overview</li><li>Querying data in NoSQL databases</li><li>Hands-on Lab: Querying data in NoSQL databases</li><li>Checklist: Querying data in NoSQL databases</li><li>Graded Quiz: Querying data in NoSQL databases</li></ul><div><strong>Module 3 - Build a Data Warehouse</strong></div><ul><li>Module Introduction and Learning Objectives</li><li>Assignment Overview</li><li>Hands-on Lab: Data Warehousing</li><li>Checklist: Data Warehousing</li><li>Hands-on Lab: Data Warehousing Reporting</li><li>Checklist: Data Warehousing Reporting</li><li>Graded Quiz: Data Warehousing &amp; Reporting</li></ul><div><strong>Module 4 - Data Analytics</strong></div><ul><li>Module Introduction and Learning Objectives</li><li>Assignment Overview</li><li>Hands-on Lab: Getting Started with Cognos Dashboard Embedded (Optional)</li><li>Dashboard Creation</li><li>Hands-on Lab: Dashboard Creation</li><li>Checklist: Dashboard Creation</li><li>Graded Quiz: Dashboard Creation</li></ul><div><strong>Module 5 - ETL &amp; Data Pipelines</strong></div><ul><li>Module Introduction and Learning Objectives</li><li>Assignment Overview</li><li>ETL &amp; Data Pipelines using Apache Airflow</li><li>Hands-on Lab: ETL</li><li>Checklist: ETL</li><li>Hands-on Lab: Data Pipelines using Apache Airflow</li><li>Checklist: Data Pipelines using Apache Airflow</li><li>Graded Quiz: ETL &amp; Data Pipelines using Apache Airflow</li></ul><div><strong>Module 6 - Big Data Analytics with Spark</strong></div><ul><li>Module Introduction and Learning Objectives</li><li>Assignment Overview</li><li>Big Data Analytics with Spark</li><li>Practice Hands-on Lab: Saving and loading a SparkML model</li><li>Hands-on Lab: SparkML Ops</li><li>Checklist: Big Data Analytics with Spark</li><li>Graded Quiz: Big Data Analytics with Spark</li></ul><div><strong>Module 7 - Final Submission</strong></div><ul><li>Final Project</li><li>Peer Review: Submit your Work and Review your Peers</li><li>Wrap Up</li><li>Reading: Congrats &amp; Next Steps</li><li>Reading: Team &amp; Acknowledgements</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>In order to be successful in this course you should have a working knowledge of: </div><ul><li>Relational and NoSQL databases </li><li>SQL, Python, and Shell scripting </li><li>ETL with Airflow and Kafka </li><li>Data Warehousing, Cubes, Rollups, and Materialized views/MQTs </li><li>BI tools such as Cognos Analytics </li><li>Big Data analytics with Apache Spark </li></ul><div>It is highly recommended that you complete all the preceding courses in the IBM Data Engineering Professional Certificate. </div>
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Data Engineering Capstone Project

This Capstone Project is designed for you to apply and showcase your skills and knowledge in Data Engineering, including SQL, NoSQL, RDBMS, Bash, Python, ETL, Data Warehousing, BI tools, and Big Data. 

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  <div>Databases are the backbone of modern applications, powering everything from large scale enterprise systems to web applications.  <br /><br />This course gives you the comprehensive grounding you need to work with databases using Java programming. Whether you have a little or no knowledge of databases, if you’re keen to understand the different types of databases and how to work with these databases using Java, this course is for you!   <br /><br />During the course, you’ll explore the fundamentals of databases and Java Database Connectivity (JDBC). You’ll get hands-on developing database applications using Spring Boot. Plus, you’ll work with Hibernate and explore other databases such as NoSQL, SQL Graph and time series databases.  <br /><br />Throughout the course, you’ll complete hands-on labs and gain valuable practical experience applying your skills. Plus, you’ll complete a final project where you apply your knowledge to a real-world scenario; great for chatting about in interviews!  <br /><br />If you’re looking to build the job-ready skills you need to develop Java applications with databases, enroll today.<br /><br /></div><h1>Course Syllabus</h1><div><strong>Module 1: Fundamentals of Databases and JDBC</strong><br /><br />This module introduces the foundational concepts of relational databases and their integration with Java using JDBC. It covers database design basics, setting up connections, and performing CRUD operations programmatically.<br /><br /><strong>Module 2: Spring Boot for Database Applications</strong><br /><br />This module explores the use of Spring Boot to simplify database-driven application development. It covers configuring Spring Boot with databases, using Spring Data JPA for CRUD operations, and exposing database functionality through RESTful APIs. The module encourages active participation through practical exercises.<br /><br /><strong>Module 3: Hibernate and Other Databases</strong><br /><br />This module provides an in-depth understanding of Hibernate as an ORM framework. It also explains the role in handling complex database interactions. Learners will explore advanced entity relationships, caching, and query optimization. It also walks you through other modern data storage solutions such as NoSQL databases, Graph databases, and time series databases.<br /><br /><strong>Module 4: Final Project and Course Wrap-up</strong><br /><br />This module provides an opportunity for learners to develop the backend for a retail management system to be exclusively used by the admin users. You’ll be using Java, Spring Boot, and Hibernate. The system will use MySQL for structured data and a NoSQL database for unstructured data. <br /><br /></div><h1>Course Objectives</h1><ul><li>Understand databases and their role in applications</li><li>Handle basic CRUD operations with JDBC</li><li>Create connection pools and optimize them using Java</li><li>Apply abstraction using Spring Boot applications to simplify database integration</li><li>Learn to expose the database CRUD operations through RESTful APIs</li><li>Understand Java Persistence API (JPA)</li><li>Configure Hibernate in Spring Boot</li><li>Handle Advanced Entity Relationships with ORM using Hibernate</li><li>Understand the differences between the HQL and SQL query languages</li><li>Use NoSQL databases for CRUD operations<br />Configure NoSQL databases in Spring Boot</li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>To get the most out of this course, you should be comfortable with the following topics and technologies:</div><ul><li>Java programming</li><li>Spring Boot</li></ul><div><br />This course requires knowledge of software engineering and Java development fundamentals, experience with using the Git and GitHub environments, and a good working knowledge of the Spring framework.</div>
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Java Development with Databases 

This course gives you the comprehensive grounding you need to work with databases using Java programming. Whether you have a little or no knowledge of databases, if you’re keen to understand the different types of databases and how to work with these databases using Java, this course is for you!   

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  <div>This course is meant for professionals with a background in data engineering, database administration, system architecture, and data analysis. <br /><br /></div><ul><li>Gain hands-on experience working with data architecture that you can showcase in your portfolio and talk about in interviews.  </li><li>Analyze and assess existing data architecture in alignment with business objectives. </li><li>Implement data migration and integration solutions, including building data pipelines. </li><li>Apply data governance and security protocols, ensuring compliance and data protection.  </li></ul><div><br /></div><div><br /></div>
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Data Architect Capstone Project

Gain practical, real-world experience in data architecture through this hands-on capstone project course, developing skills highly valued by employers. This is an ideal opportunity to demonstrate to recruiters that you have the skills and knowledge they’re looking for.  During this course, you’ll apply all that you’ve learned throughout the Data Architect Professional Certificate.As you work through the course, you’ll evaluate, design, migrate, and integrate enterprise data systems through a case study.

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  <div><strong>Course Description:</strong> <br /><br /></div><div>Generative AI (gen AI) is transforming digital marketing by enhancing efficiency, personalization, and content creation. According to a McKinsey report, AI-driven advancements could boost marketing productivity by 5–15% of total spending, adding approximately $463 billion annually. <br /><br /></div><div>Digital marketers who can leverage gen AI are in high demand, and this course gives you sought-after gen AI skills businesses need. <br /><br /></div><div>In this course, you’ll explore how gen AI automates content creation, enhances ad copy, and personalizes email campaigns and social media posts. You’ll learn gen AI-driven techniques for audience segmentation, keyword research, SEO optimization, and digital advertising to create targeted, data-driven campaigns. The course also covers AI’s role in e-commerce and email marketing, emphasizing automation, personalization, and improved campaign performance. <br /><br /></div><div>In addition, you’ll examine ethical considerations, challenges, and best practices for AI integration in digital marketing.  <br /><br /></div><div>Boost your marketing career with in-demand gen AI skills. Enroll today to enhance your resume and stand out to employers.  <br /><br /></div><div> <br /><br /></div><div><strong>Course objectives:</strong> <br /><br /></div><div>Upon successful completion of this course, learners will be able to: <br /><br /></div><ol><li><strong>Understand the fundamentals of generative AI:</strong> Gain a clear understanding of the core concepts and technologies behind generative AI and its potential applications in digital marketing. </li><li><strong>Leverage AI for content creation:</strong> Learn how to use AI tools to generate high-quality content, including articles, social media posts, product descriptions, and email copy, efficiently and effectively. </li><li><strong>Optimize digital advertising with AI:</strong> Explore how to use AI for audience segmentation, ad creative optimization, and personalized advertising campaigns across various platforms. </li><li><strong>Enhance SEO with AI:</strong> Discover how AI can be used for keyword research, content optimization, technical SEO audits, and competitor analysis to improve search engine rankings. </li><li><strong>Improve e-commerce experiences with AI:</strong> Learn how to use AI for personalized product recommendations, customer service chatbots, and targeted marketing campaigns to enhance the customer journey. </li><li><strong>Master AI-powered email marketing:</strong> Explore how to use AI to generate engaging email content, personalize email campaigns, and optimize email marketing strategies for better results. </li></ol><div> <br /><br /></div><div><strong>Course learning objectives for Coursera</strong> <br /><br /></div><ul><li>Job-ready, practical skills for leveraging generative AI in digital marketing </li><li>Generative AI tools and techniques for creating marketing content and campaigns </li><li>AI-driven strategies for improving SEO, digital advertising, and e-commerce experiences </li><li>Generative AI techniques to create engaging and personalized email content and targeted marketing strategies </li></ul><div><br /> <br /><br /></div>
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Generative AI: Accelerate your Digital Marketing Career

In this course, you’ll explore how gen AI automates content creation, enhances ad copy, and personalizes email campaigns and social media posts. You’ll learn gen AI-driven techniques for audience segmentation, keyword research, SEO optimization, and digital advertising to create targeted, data-driven campaigns. The course also covers AI’s role in e-commerce and email marketing, emphasizing automation, personalization, and improved campaign performance. In addition, you’ll examine ethical considerations, challenges, and best practices for AI integration in digital marketing.  

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  <div><strong>About this course</strong></div><div>This short, self-paced course was created to teach you how to effectively solve and analyze problems with critical and creative thinking. This course will equip you with invaluable skills that you will have to use in your day-to-day work, especially in office or business-related jobs.<br /><br /></div><div>Critical and creative thinking are important because when you encounter a problem, you need to be able to think of different ways to solve the problem, as well as have the ability to recognize and choose the best solution possible. There are certain steps you must take before finding a solution; the most important being analyzing the problem. By taking this course, you will learn a specifically designed five-step method that will help you find the root cause of a problem. By learning the process you must go through to find the problem, you will also get one step closer to finding a solution.<br /><br /></div><div>This short course is suitable to be taken by everyone – students, career starters, even experienced professionals and managers who want a skill refresher. By taking this course, you will gain the fundamental skills needed in order to successfully solve any problem and the confidence to achieve professional success.<br /><br /></div><div>This course is part of the People and Power Skills Professional Certificate from IBM, which consists of a series of short courses to help you develop essential soft skills for success in the workplace.<br /><br /></div><div><strong>What you'll learn</strong></div><ul><li>Apply critical and creative thinking to solve problems</li><li>Utilize the 5-step process in order to find the root cause of a problem</li><li>Analyze a problem and identify all possible solutions</li><li>Identify the best solution to implement when solving a problem in the workplace</li></ul><div><strong>Prerequisites<br /></strong><br /></div><div>No previous experience required! This course can be taken by anyone regardless of professional experience.<br /><br /></div><div><strong>Curriculum</strong></div><div><br /></div><div>The course is composed of the following lessons:</div><div>- Solve Problems with Creative and Critical Thinking: An Overview</div><div>- About the course</div><div>- Topic 1: What’s the problem?</div><div>- Topic 2: What’s really going on?</div><div>- Topic 3: What are my options?</div><div>- Topic 4: What is the best solution?</div><div>- Topic 5: What will success look like?</div><div>- Course-end Assessment</div>
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Solving Problems with Creative and Critical Thinking

Use critical and creative thinking to analyze and solve any problem you might encounter in the workplace, an extremely valuable skill to have in any profession!

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  <div>In this course, you will focus on Node.js and Express. Specifically, you will</div><ul><li>develop applications using asynchronous callbacks and promises</li><li>create REST APIs and perform CRUD operations</li><li>implement authentication and session management</li></ul><div>Throughout the course, you will complete numerous hands-on labs to gain practical experience. At the end of the course, you will demonstrate your Node skills with a final project to build your portfolio.<br /><br /></div><div>This course will help you succeed as a back-end or full-stack developer. It suits those in IT looking to step up in their careers or new graduates seeking to establish their server-side skills. This course suits those who need to manage cloud-centric projects.<br /><br /></div><div>Note: This course requires knowledge of JavaScript and Git.</div><div><br />After completing this course, you will be able to: </div><ul><li>Create server-side applications using the Node.js JavaScript runtime.</li><li>Extend your Node.js applications with third-party packages, including Express.</li><li>Use npm to manage Node.js packages in your Node.js application.</li><li>Write asynchronous callback functions and promises to complete asynchronous operations.</li></ul><h1>Course Syllabus</h1><div>Module 1: Introduction to Databases and Working with Node</div><ul><li>Lesson 1: Introduction to Databases  </li><li>Lesson 2: Module Wrap-up and Quiz</li></ul><div>Module 2: Working with Node and Databases</div><ul><li>Lesson 1: Introduction to working with NoSQL Databases and Node</li><li>Lesson 2: Module Wrap-up and Quiz</li></ul><div>Module 3: Working with REST APIs and advanced Node concepts</div><ul><li>Lesson 1: Restful APIs</li></ul><div>Module 4: Advanced development concepts with Node</div><ul><li>Lesson 1: Error Handling and Middleware </li><li>Lesson 2: Wrap-up and Quiz</li></ul><div>Module 5: Final Project</div><ul><li>Lesson 1: Final Project</li></ul><h1>General Information</h1><div>This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.<br /><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>The following skills are required to be successful with this course: </div><ul><li>Working knowledge of JavaScript</li><li>Working knowledge of Node and Express</li></ul><div><br /><br /></div>
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Back-end Application Development with Node.js and Express

In a recent Stack Overflow survey, Node.js was used by about 50% of the developers who answered the survey, making it the most used server-side technology. Express ranked as the fourth most popular web technology overall, making it the most popular server-side web framework.

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  <div>Interested in learning the complete process of developing cloud-native applications, from start to finish? Then this course is for you!<br /><br /></div><div>This course covers essential cloud concepts like Cloud Native Applications and Cloud Native Computing Foundations (CNCF) and their impact on cloud app developers. You'll also dive into specific tools and techniques for building these apps.<br /><br /></div><div>You’ll then move on to improving your organization’s culture through DevOps. This involves maintaining consistent behavior and high code quality through approaches like test-driven and behavior-driven development. It will help you establish uniform practices and uphold exemplary code quality.<br /><br /></div><div>Additionally, the course explains the benefits of implementing Continuous Integration (CI) and Continuous Delivery (CD) alongside Agile and Scrum methodologies. You'll also understand how aligning teams and promoting autonomy contributes to building superior systems.<br /><br /></div><div>The course also covers MongoDB principles in depth and explores IBM Cloudant, including its architecture, technologies, advantages, and common use cases.<br /><br /></div><div>Finally, the course concludes with a hands-on project. In this project, you'll deploy and modernize an application, refine user stories, add new features, and redeploy using CI/CD. This project will serve as a valuable addition to your portfolio, showcasing your newly acquired skills.<br /><br /></div><h1>Course Syllabus</h1><div><strong>Module 1: Introduction to Cloud Native</strong></div><ul><li>Welcome to Developing Cloud Native Applications</li><li>Cloud Computing Overview</li><li>Cloud Native Applications</li><li>CNCF</li><li>Hybrid Cloud (Optional)</li><li>Modernizing with Cloud Native Technologies</li></ul><div><strong>Module 2: DevOps &amp; CI/CD</strong></div><ul><li>Introduction to DevOps &amp; CI/CD</li><li>Definition of DevOps</li><li>Essential Characteristics of DevOps</li><li>Test Driven Development</li><li>Behavior Driven Development (BDD)</li><li>What Is CI/CD?</li><li>Benefits of CI/CD</li><li>Tools for DevOps, CI and CD</li><li>DevOps Tools Categories and Their Available Tools</li><li>Hands-on Lab: CI/CD in Action</li><li>Everyone is Responsible for Success</li></ul><div><strong>Module 3: Agile and Scrum</strong></div><ul><li>Introduction to Agile Development and Scrum</li><li>Agile Principles</li><li>Scrum Overview</li><li>Organizational impact of Agile</li><li>Mistaking Iterative Development for Agile</li><li>Destination Unknown</li><li>Agile Roles and the Need for Training</li><li>Creating Good User Stories</li><li>Effectively using Story Points</li><li>Building the Product Backlog</li><li>Sprint Planning</li><li>The Sprint Review</li><li>The Sprint Retrospective</li><li>Hands-on Lab: Get Set Up in ZenHub</li><li>Hands-on Lab: Creating stories in ZenHub</li></ul><div><strong>Module 4: NoSQL Databases in Cloud</strong></div><ul><li>Overview of NoSQL</li><li>Overview of MongoDB</li><li>Advantages of MongoDB</li><li>Use Cases for MongoDB</li><li>MongoDB in the Cloud IDE</li><li>Hands-on Lab: Getting Started with MongoDB</li><li>Hands-on Lab: MongoDB CRUD</li><li>CRUD Operations</li><li>Hands-On Lab: Accessing MongoDB from Python (Optional)</li></ul><div><strong>Final Project</strong></div><ul><li>Final Project Overview</li><li>Final Project Instruction</li><li>Peer Graded Assignment</li></ul><h1><strong>What you'll learn</strong></h1><div>After completing this course, a learner will be able to:</div><ul><li>Explore cloud-native applications concepts and methodologies.</li><li>Recognize CNCF’s impact, and analyze the benefits of CI/CD, Agile, and Scrum.</li><li>Recognize the DevOps culture and its significance.</li><li>Differentiate between TDD and BDD and explore the principles and characteristics of MongoDB.</li><li>Implement CI/CD practices and connect and integrate user stories to facilitate the Agile development process.</li><li>Enhance application functionality using tools and techniques.</li><li>Perform basic data operations using MongoBD and CRUD operations</li></ul><h1>Recommended Skills Prior to Taking this Course</h1><ul><li>Familiarity with Cloud concepts and terminology </li><li>Basic literacy in Cloud Application Development </li><li>Basic knowledge of HTML and Javascript </li></ul>
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Get Started with Cloud Native, DevOps, Agile, and NoSQL

Learn to develop cloud-native applications from scratch in this comprehensive course. Covering key cloud concepts, tools, and techniques, you'll also explore DevOps culture, CI/CD practices, and MongoDB principles.

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  <div>The emergence of cloud computing has brought about a lot of changes to modern software development. Rather than large annual releases on physical mediums like CDs, modern software is increasingly released on the cloud, provided cost reduction, decreased time to market, and increased agility. These changes allow organizations to keep pace with their competitors and bring new innovations to market in ways that previously would have been unthinkable.<br /><br /></div><div>Rather than building large applications, known as monoliths, that perform all the functionality, microservices break down larger applications into smaller pieces that are independently maintainable and scalable, providing a host of benefits. This architecture is used at the largest software organizations in the world, because it provides cost benefits, team autonomy, and so many other advantages.<br /><br /></div><div>By the end of this course, you will</div><ul><li>Have a solid foundation for microservices and serverless</li><li>Deploy microservices on the cloud</li><li>Integrate microservices with serverless compute</li></ul><div>This course will introduce you to 12-factor apps and microservices, concepts that emerged to help organizations work better and faster in a cloud-native manner. You’ll then learn about serverless computing—how it works, what value it brings, and what are specific serverless technologies. You’ll get hands-on with IBM Cloud Functions, a serverless platform on IBM Cloud that lets you develop serverless apps with ease. Finally, you will learn to build and deploy applications using container images on the code engine.</div><div>The course contains several hands-on labs which allow you to apply the content you learn.</div><div><br /></div><div><strong>What you'll learn?</strong></div><ul><li>Identify the twelve factors and describe how they map to the software delivery lifecycle</li><li>Create and test Swagger documentation for REST API endpoints</li><li>Create and test a serverless function</li><li>Build and deploy applications using container images on the code engine</li></ul><div><strong>Prerequisites<br /></strong><br /></div><div>The prerequisites for this course include basic computer and cloud literacy as well as an understanding of core cloud concepts. In addition, understanding of the command line and how to use shell commands will greatly benefit you during this course.<br /><br /></div><div><strong>Curriculum</strong></div><div><br /></div><div><strong>Welcome</strong></div><ul><li>Reading: Welcome to Microservices and Serverless</li><li>Reading: General Information</li><li>Reading: Learning Objectives and Syllabus</li><li>Reading: Grading Scheme</li><li>Video: Course Introduction</li></ul><div><strong>Module 1: Introduction to MicroServices</strong></div><ul><li>Reading: Module Introduction and Learning Objectives</li><li>Video: Twelve-Factor App Methodology</li><li>Video: What are Microservices?</li><li>Video: Comparison of Monolith vs SOA vs Microservices</li><li>Video: Microservices Patterns</li><li>Reading: Microservices Anti-Patterns</li><li>Hands-on Activity: Modernize JPetStore with Microservices</li><li>Reading: Summary and Highlights</li><li>Practice Quiz: Introduction to Microservices</li><li>Graded Quiz: Introduction to Microservices</li><li>Reading: Module 1 Glossary - Introduction to Microservices</li></ul><div><strong>Module 2: Web API Essentials: REST API and GraphQL</strong></div><ul><li>Reading: Module Introduction and Learning Objectives</li><li>Video: What is REST</li><li>Video: Introduction to API Gateway</li><li>Video: Creating REST APIs</li><li>Hands-on Lab: Creating a HELLO WORLD REST API with Python &amp; Flask</li><li>Video: Making API Requests using cURL and Postman</li><li>Hands-on Lab: CRUD Operations with Python</li><li>Video: Documenting and Testing REST API Swagger</li><li>Hands-on Lab: Documenting and Testing REST API Swagger</li><li>Hands-on Lab: Optional - Querying with GraphQL</li><li>Reading: Summary and Highlights</li><li>Reading: Module 2 Glossary - Web API Essentials REST</li><li>Practice Quiz: Web API Essentials - REST API and GraphQL</li><li>Graded Quiz: Web API Essentials - REST API and GraphQL (10 Questions)</li></ul><div><strong>Module 3: Serverless Overview</strong></div><ul><li>Reading: Module Introduction and Learning Objectives</li><li>Video: Introduction to Serverless Computing</li><li>Video: Serverless Pros and Cons</li><li>Video: Introduction to the FaaS Model</li><li>Video: The Serverless Framework</li><li>Video: Serverless Reference Architecture and Use Cases</li><li>Video: Popular Serverless Platforms</li><li>Reading: AWS Lambda</li><li>Hands-on Lab: Optional - Signing up for an AWS Account</li><li>Hands-on Lab: Optional - Creating an AWS Lambda</li><li>Reading: Summary and Highlights</li><li>Reading: Module 3 Glossary - Serverless Overview</li><li>Practice Quiz: Serverless Overview</li><li>Graded Quiz: Serverless Overview (10 Questions)</li></ul><div><strong>Module 4: Create and Deploy Microservices using Serverless</strong></div><ul><li>Reading: Module Introduction and Learning Objectives</li><li>Video: IBM Cloud Code Engine</li><li>Video: Project, Application, Build, and Jobs</li><li>Hands-on Lab: Deploying your first application on Code Engine</li><li>Video: Building Container Images for Microservices</li><li>Video: Deploying and Running Applications</li><li>Hands-on Lab: Deploying your first Docker image on Code Engine</li><li>Video: Updating Deployed Applications</li><li>Hands-on Lab: Deploy, Update, and Scale Microservices using Serverless</li><li>Reading: Summary and Highlights</li><li>Graded Quiz: Create and Deploy Microservices (10 Questions)</li><li>Reading: Module 4 Glossary - Create and Deploy Microservices</li><li>Practice Quiz: Create and Deploy Microservices</li></ul><div><strong>Final Assignment:</strong></div><ul><li>Final Project Overview and Grading Criteria</li><li>Final Project: Serverless Web Application and API</li><li>Peer-Graded Final Assignment: Serverless Web Application and API</li></ul><div><strong>Module 5: [Optional] OpenShift Essentials/Working with OpenShift and Istio</strong></div><ul><li>Reading: Module Introduction</li><li>Video: OpenShift Recap</li><li>Video: Service Mesh and Istio</li><li>Video: Microservices with OpenShift</li><li>Video: Red Hat Marketplace</li><li>Hands-on Lab: OpenShift</li><li>Reading: Summary and Highlights</li><li>Practice Quiz</li><li>Practice Quiz: OpenShift Essentials/Working with OpenShift and Istio</li></ul>
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Microservices and Serverless for Application Developers

Design, develop, deploy, manage and secure applications and solutions on public, private or hybrid cloud platforms

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  <div><strong>Imagine having an AI assistant at your fingertips</strong> that can understand complex documents and answer your questions accurately without requiring you to read through hundreds of pages. What if you could simply upload a technical report, legal document, or research paper and get precise answers based on its content? This is the power of combining <strong>multi-agent RAG systems with document intelligence</strong>.</div><div><br />In this hands-on lab, you'll build a sophisticated question-answering system that makes document comprehension accessible to everyone in your organization—from researchers analyzing technical papers to legal teams extracting insights from contracts, all without the tedious manual review.</div><div><br /></div><h1><strong>Project Overview</strong></h1><div>This lab teaches you to create an intelligent document processing system that handles the entire question-answering workflow:</div><div>1️⃣ <strong>Document Processing &amp; Chunking</strong> - Extract text from PDFs and other formats, process into searchable chunks with caching for performance<br />2️⃣ <strong>Hybrid Retrieval</strong> - Combine keyword-based BM25 and semantic vector search for optimal document retrieval<br />3️⃣ <strong>Multi-Agent Verification</strong> - Use specialized agents for relevance checking, research, and fact verification<br />4️⃣ <strong>LangGraph Orchestration</strong> - Coordinate agent interactions with conditional workflows and feedback loops</div><div><br />By connecting specialized agents through LangGraph, you'll create a seamless experience where users can upload documents and get verified, accurate answers using IBM's Granite AI's powerful models.</div><div><br /></div><h1><strong>What You'll Learn</strong></h1><div>By completing this lab, you will:</div><ul><li>Design effective <strong>document processing pipelines</strong> with caching and deduplication</li><li>Build <strong>hybrid retrieval systems</strong> that balance keyword precision with semantic understanding</li><li>Create <strong>specialized AI agents</strong> for different stages of the question-answering process</li><li>Implement <strong>verification mechanisms</strong> to ensure factual accuracy</li><li>Orchestrate <strong>complex workflows</strong> with conditional branching and feedback loops</li></ul><div><br /></div><h1><strong>Who Should Do This Lab</strong></h1><div>This project is ideal for:</div><ul><li><strong>Developers</strong> looking to build practical document intelligence applications</li><li><strong>Data scientists</strong> wanting to make document insights accessible to non-technical colleagues</li><li><strong>AI enthusiasts</strong> interested in creating trustworthy information retrieval systems</li></ul><div>No advanced ML expertise required—basic Python knowledge and curiosity about RAG applications are all you need.</div><div><br /></div><h1><strong>What You Need</strong></h1><div>✅ <strong>A browser</strong> to access the lab environment<br />✅ <strong>Basic Python knowledge</strong> (understanding functions and data structures)<br />✅ <strong>Basic LangChain and LLM knowledge</strong><br />✅ <strong>Sample documents</strong> (we provide examples, or bring your own PDFs)</div><div><br />By the end of this project, you'll have built an AI document assistant that transforms how people interact with information—enabling anyone to ask questions about complex documents and receive verified, accurate answers in seconds.</div>
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Multi-Agent RAG Smart Document QA with Docling & LangGraph

Build a multi-agent RAG document question-answering system using LangGraph workflows and Docling for document processing. Learn to extract content from PDFs with Docling, implement hybrid retrieval combining BM25 and vector search, and create specialized agents for relevance checking, research, and verification. Integrate with IBM WatsonX AI for embeddings and language models to generate accurate answers from documents. Master techniques for document chunking, caching, fact-checking responses, and handling complex questions through coordinated agent interactions.

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  <div><br />In this capstone course, you’ll demonstrate your ability to solve problems relevant to data-related roles, which you can then showcase in your portfolio and share with prospective employers. You will apply the skills you’ve learned throughout this data management program with this hands-on culminating project. </div><div><br />The project provides you the opportunity to solidify your proficiency when working with various aspects of data and integrate them into a single project. You’ll apply many industry-specific tools and industry-standard best practices to a real-world-inspired scenario. As part of this process, you utilize relational databases and SQL queries. You’ll also manipulate data using spreadsheets and BI tools to create visualizations and a dashboard to readily communicate your data insights. <br /><br /></div><h1>Course Syllabus</h1><div><strong>Module 1: Introduction and Data Preparation and Exploratory Analysis with Excel </strong></div><ul><li>Video: Course Introduction </li><li>Reading: Course Overview </li><li>Reading: Data Platform Architecture </li><li>Reading: Helpful Tips for Course Completion </li><li>Video: Assignment Overview: Data Cleaning &amp; Transformation Using Excel </li><li>Reading: Data Set Description </li><li>Lab 1: Data Cleaning &amp; Transformation </li><li>Checklist: Data Cleaning &amp; Transformation Using Excel  </li><li>Video : Assignment Overview: Data Visualization Using Excel </li><li>Lab 2: Data Visualization Using Excel </li><li>Checklist: Data Visualization Using Excel </li><li>Module 1: Summary and Highlights </li><li>Module 1 Graded Quiz: Data Preparation and Exploratory Analysis with Excel </li></ul><div><strong>Module 2: Relational Database (OLTP) </strong></div><ul><li>Video : Assignment Overview: Data Setup Using Relational Database </li><li>Lab 3: Data Setup Using Relational Database  </li><li>Checklist: Data Setup Using Relational Database </li><li>Video : Assignment Overview: Data Analysis with SQL </li><li>Lab 4: Data Analysis with SQL  </li><li>Checklist: Data Analysis with SQL  </li><li>Module 2: Summary and highlights </li><li>Module 2 Graded Quiz: Data Setup Using Relational Database (OLTP) </li></ul><div><strong> Module 3: Data Warehouse  </strong></div><ul><li>Video : Assignment Overview: Data Warehouse Design &amp; Setup </li><li>Lab 5: Data Warehouse Design &amp; Setup </li><li>Checklist: Data Warehouse Design &amp; Setup  </li><li>Video : Assignment Overview : Data Warehouse Analysis with SQL </li><li>Lab 6: Data Warehouse Analysis with SQL </li><li>Checklist: Data Warehouse Analysis with SQL </li><li>Module 3: Summary and Highlights </li><li>Module 3 Graded Quiz: Data Warehouse </li></ul><div><strong> Module 4: Data Privacy, Security, Governance, Risk, and Compliance </strong></div><ul><li>Video : Data Integration, Governance and Security Implementation for FinPro Bank </li><li>Reading: Recommendations on Integration, Governance and Compliance </li><li>Checklist: Data Privacy, Security, Governance, Risk, and Compliance </li><li>Video : Assignment Overview: Data Privacy, Security, Encryption, and Role-Based Access </li><li>Lab 7: Implementing Data Privacy, Security, Encryption, and Role-Based Access using MySQL </li><li>Checklist: Implementing Data Privacy, Security, Encryption, and Role-Based Access </li><li>Module 4: Summary and Highlights </li><li>Module 4 Graded Quiz: Data Privacy, Security, Governance, Risk, and Compliance </li></ul><div><strong> Module 5: Data Vizualization and Dashboard </strong></div><ul><li>Video: Assignment Overview: Business Intelligence Using Tableau </li><li>Lab: Business Intelligence Using Tableau </li><li>Checklist: Business Intelligence Using Tableau </li><li>Video: Assignment Overview: Story Dashboard </li><li>Lab 9: Story Dashboard </li><li>Checklist: Story Dashboard </li><li>Module 5: Summary and Highlights </li><li>Module 5 Graded Quiz: Data Visualization and Dashboards </li></ul><div><strong>Module 6: Final Submission &amp; Evaluation </strong></div><ul><li>Structure of a presentation</li><li>Best Practices for Presenting Your Findings</li><li>[Optional] Hands-on Lab: Getting started with PowerPoint for the web </li><li>[Optional] Hands-on Lab: Save Your PowerPoint Presentation as a PDF  </li><li>Reading: Final Project Instructions  </li><li>Assignment: Submit your work and review your peers </li><li>Reading: Congratulations &amp; Next Steps </li><li>Reading: Team and Acknowledgements </li><li>Video: Resume &amp; Job Search Tips </li><li>Reading: Guide to developing a Personal Portfolio </li><li>Video: Interview Preparation </li></ul><h1>What you'll learn</h1><ul><li>Integrate valuable applied data management skills employers need for a wide range of data-related roles in a culminating project. </li><li>Gain hands-on experience using industry-specific data tools including Excel, SQL, PostgreSQL, Tableau, etc.</li><li>Demonstrate best practices and apply those methodologies through industry-standard processes, data design, governance, security, and reporting.</li><li>Showcase your ability to solve problems relevant to working with data that you can discuss with colleagues and prospective employers.</li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>We recommend that you complete all the previous courses in IBM Data Management Professional Certificate  before starting this course.</div><div><br /></div>
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Data Management Capstone Project

This capstone project underscores the vital role of a Data Manager in steering FinPro Bank toward a data-centric culture. By overseeing every aspect of the data management process—from data cleaning in Excel to building interactive dashboards in Tableau—the Data Manager ensures that data is accurate, secure, and accessible at each stage.

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  <div>This course will equip future Product Owners with the essential skills and strategies needed to maximize product value and drive innovation within Agile environments. This course includes a comprehensive exploration of topics such as product roadmaps, release planning, KPI development, and governance. As a result of this course, learners will be able to align product development with business goals.  <br /><br /></div><div>In this course, learners will explore the product owner role as part of product governance. They will learn that product owners’ responsibilities include ensuring the product delivers value while minimizing risks and maintaining accountability. They will gain the necessary knowledge to align product development with business goals. With regards to Agile, they will learn about the minimal viable product, or MVP, and how to develop the MVP to support the product vision by making use of the Scrum framework. You will learn how to track your Scrum team’s velocity to effectively balance strategic priorities and operational realities.  <br /><br /></div><div>The course also delves into technical practices, including acceptance testing, wireframing, and technical writing, which enable product owners to communicate effectively with stakeholders and development teams. The course also addresses technical debt, why it matters, and how a product owner can efficiently handle this debt by integrating its reduction strategically into sprints. <br /><br /><strong>Course Learning Objectives </strong></div><ul><li>Job-ready skills and tools needed for success in a Product Owner role  </li><li>Practical knowledge that transfers across industries that implement Scrum development teams  </li><li>Industry recognized strategies applicable to product development in an Agile environment  </li><li>Develop expertise for guiding Scrum teams to build products aligned with organizational goals  </li></ul><h1>Course Syllabus</h1><div><strong>Module 1: Introduction to Product Ownership and Strategic Foundations </strong></div><ul><li>Vdeo: Course Introduction</li><li>Reading: Course Overview</li><li>Video: Understanding the Product Owner Role  </li><li>Reading: Product Owner Skills Checklist  </li><li>Expert Viewpoint: Role of the Product Owner  </li><li>Video: Product Value Maximization  </li><li>The Product Owner's Impact on Value Delivery  </li><li>Practice Quiz  </li><li>Video: Eliciting Requirements  </li><li>Video: Scenario Development  </li><li>Video: Technical Debt Management  </li><li>Expert Viewpoint: Managing Technical Debt  </li><li>Lab/Activity: Technical Debt  User Stories </li><li>Practice Quiz:</li><li>Reading: Module 1 Summary and Highlights  </li><li>Module 1 Glossary</li><li>Module 1 Graded Quiz </li></ul><div><strong>Module 2: Product Planning, Roadmaps, and Metrics  </strong></div><ul><li>Video: Governance Planning and Budgeting  </li><li>Expert Viewpoint: Role of the Product Owner in Strategic Planning  </li><li>Demo Video: Lean Canvas Planning  </li><li>Lab: Lean Canvas Creation  </li><li>Video: KPI Development and Management  </li><li>Practice Quiz</li><li>Demo Video: Product Roadmaps  </li><li>Reading: Scaling Agile Frameworks </li><li>Video: Release Planning  </li><li>Expert Viewpoint: Agile Scaling and Product Release Planning</li><li>Demo Video: Release Planning Example  </li><li>Lab: Network Diagramming Method  </li><li>Practice Quiz </li><li>Reading: Module 2 Summary and Highlights  </li><li>Module 2 Glossary</li><li>Graded Quiz</li></ul><div><strong>Module 3: Agile Practices, Technical Skills, and Communication </strong></div><ul><li>Video: Technical Writing and Communication  </li><li>Expert Viewpoint: Tips for effective technical writing  </li><li>Demo Video: Persona Development  </li><li>Video: Acceptance Testing and UX Integration  </li><li>Video: Managing Customer Feedback  </li><li>Practice Quiz  </li><li>Reading: Product Management Lifecycle Overview  </li><li>Video: Working with Product Management Lifecycle  </li><li>Video: Effective Product Owner and Product Manager collaboration tips  </li><li>Demo Video: Velocity Management and Estimation Techniques  </li><li>Lab: Velocity Planning</li><li>Video: Plan velocity based on a scenario</li><li>Wireframes, Mock-ups, and Prototypes  </li><li>Video: Using Visual Tools to Communicate Product Vision  </li><li>Practice Quiz </li><li>Reading: Module 3 Summary and Highlights  </li><li>Module 3 Glossary</li><li>Graded Quiz</li></ul><div><strong>Module 4: Guided Project, Final Assessment, and Course Wrap-Up </strong></div><ul><li>Reading: Final Project Overview  </li><li>Final Project: AutoproSolutions Inc,</li><li>Final Quiz</li><li>Video: Course Wrap-Up  </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>Some experience or familiarity with product development processes or related roles, Exposure to Agile-related concepts and terms </div>
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Product Owner: Essential Skills and Tools for Innovation

Product Owners play a critical role in ensuring the success of products and the realization of business value in an increasingly complex and competitive landscape.  This course teaches prospective Product Owners essential skills and strategies that drive product innovation within Agile environments, such as product roadmaps and key performance indicators. 

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  <div>Collaboration and communication are at the core of a Product Owner's function in driving product success. Communicating effectively is essential in handling stakeholders, building collaboration, and delivering customer value. </div><div><br />This course is perfect for those in product owner roles.  Whether you are new to Agile or sharpening your expertise: <br /><br /> - Explore Agile and Scrum frameworks to manage team dynamics and stakeholder needs. <br /><br /> - Learn to break down requirements into epics, user stories, and acceptance criteria. <br /><br /> - Discover advanced tools for virtual collaboration, conflict resolution, and global team management. <br /><br /> - Develop techniques for prioritizing deliverables, managing trade-offs, and protecting the Voice of the Customer (VOC). <br /><br /> - Master the key communication models and practices <br /> <br />Through exercises and expert insights, develop: <br /><br /> - Collaborative workshop facilitation. <br /><br /> - Skills in managing product backlogs and deliverables. <br /><br /> - Strategies for working with cross-functional teams, DevOps, and diverse communication styles. <br /> <br />By the end of this course, you'll confidently apply collaboration, negotiation, and communication strategies to manage products effectively in complex team environments. Enroll soon to elevate your skills and drive product success!<br /><br /></div><div><strong>You will learn:</strong> </div><ul><li>Effective communication strategies to represent the Voice of the Customer within Agile teams and organizations. </li><li>Stakeholder engagement strategies to gather feedback, validate assumptions, make effective decisions, and influence results.  </li><li>Negotiation and conflict resolution skills to navigate competing priorities and viewpoints effectively. </li><li>Facilitation and leadership skills to effectively lead product discovery sessions, collaborative workshops, and decision-making processes. </li></ul><h1>Course Syllabus</h1><div><strong>Module 1: Foundations of Collaboration</strong></div><ul><li>Video: Course Introduction </li><li>Reading: Course Overview</li><li>Reading: How to Make the Most from this Course </li><li>Video: Define the Communications Model</li><li>Expert Viewpoints Video: Meet the Experts</li><li>Lab: Collaboration and Product Vision</li><li>Video: The Need for Effective Communications Planning</li><li>Expert Viewpoints: Communications: Significance of Planning</li><li>Demo Video: Communication Matrix: Best Practices and Product Owner’s Role</li><li>Practice Quiz: Foundations of Collaboration</li><li>Expert Viewpoints: The Importance of Communication and Collaboration </li><li>Summary and Highlights: Foundations of Collaboration</li><li>Graded Assignment: Graded Quiz: Foundations of Collaboration</li></ul><div><strong>Module 2: Advanced Techniques in Collaboration and Product Development </strong></div><ul><li>Video: Elements of a Cross-Functional Team</li><li>Video: Team Development and Management Objectives</li><li>Reading: The 5 Dysfunctions of a Team Overview by Patrick Lencioni</li><li>Reading: How a Product Owner Interacts with the Scrum Team</li><li>Video: Developing an Agile Mindset  </li><li>Reading: Compare and Contrast Program Management Frameworks</li><li>Video: Breaking Down Requirements</li><li>Lab: Breaking Down Requirements </li><li>Reading: Quick Overview of the INVEST Model for Creating Good User Stories</li><li>Expert Viewpoints: Using Models and Guidelines (Reality versus Theory)</li><li>Video: Balancing the Needs of Scrum Team, Stakeholders, and Customers</li><li>Practice Quiz: Setting the foundation for Scrum (Team Dynamics, Agile Mindset, Stakeholders) Video: Kotler’s Five Levels of a Product </li><li>Video: Mastering Conflict Resolution: Five Conflict Management Styles</li><li>Lab: Scenarios of Conflict and Negotiation (Practice)</li><li>Reading: Overview of Key Marketing Needs (Collateral, Value, Velocity)</li><li>Video: Product Owner Collaboration Strategies Beyond Agile Team</li><li>Reading: Summary of the 4-Steps to Gathering and Validating Requirements</li><li>Video: Eliciting and Validating Requirements</li><li>Lab: Using the Tuckman Ladder Model to Help Prepare for Technical Readiness</li><li>Demo Video: RACI Matrix: A Guide to Effective Stakeholder Management in Agile Projects</li><li>Practice Quiz: Understanding Roles and Styles in Negotiation</li><li>Reading: Summary and Highlights: Advanced Techniques in Collaboration and Product Development </li><li>Graded Assignment: Advanced Techniques in Collaboration and Product Development </li></ul><div><strong>Module 3: Managing Product Deliverables Through Effective Collaboration </strong></div><ul><li>Reading: Methods and Tools Used to Prioritize Requirements (MoSCoW, Weighted Matrix, Dot Voting, etc)</li><li>Video: Interconnection Between Prioritization and Product Backlog</li><li>Lab: Working with Tradeoffs in Product Backlog Management</li><li>Lab: Sharing Status Updates as a Product Owner</li><li>Video: Product Owner Responsibilities in Knowledge and Document Management</li><li>Expert Viewpoints Video: Working with Global Teams and Virtual Collaboration</li><li>Reading: Survey of Tools and Methods for Virtual Collaboration (Pros and Cons)</li><li>Lab: Collaboration, Engagement, and Accountability in Global and Virtual Environments</li><li>Practice Quiz: Tools, Techniques, and Models to Manage Product Deliverables</li><li>Video: Effective Collaboration with DevOps and Scrum Master </li><li>Reading: Survey of Collaborative Problem-Solving Techniques (Pros and Cons)</li><li>Video: Managing Conflict During Problem-Solving  Discussions</li><li>Reading: Designing and Facilitating Collaborative Workshops</li><li>Lab: Planning and Facilitating Collaborative Workshops</li><li>Video: Communication Styles</li><li>Expert Viewpoints Video: Managing Conflicts and Communication Styles (Pros and Cons)</li><li>Lab: Working Effectively with Four Communication Styles</li><li>Practice Quiz: Understanding and Working with Different Perspectives</li><li>Reading: Summary and Highlights: Managing Product Deliverables Through Effective Collaboration</li><li>Graded Assignment: Graded Quiz: Managing Product Deliverables Through Effective Collaboration</li></ul><div><strong>Module 4: Final Project and Assessment</strong></div><ul><li>Final Project: Mastering Product Ownership</li><li>Final Project Submission </li><li>Expert Viewpoints: Navigating Different Roles and Styles</li><li>Course Glossary</li><li>Final Quiz</li><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>This course requires no prior experience and is suitable for anyone aspiring to pursue a career in product management. </div>
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Product Owner: Communications & Stakeholder Management

Gain hands-on experience with communication, collaboration, and business skills to engage stakeholders and manage products. Apply this knowledge to evaluate case studies effectively. 

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  <div><br />Unleash your career potential with our course on the fundamentals of the Scrum Master role. Whether you're new to Agile methodologies or seek to enhance your project management skills, this course will equip you with the knowledge and tools needed to excel as a Scrum Master. <br /><br /></div><div><br />Throughout the course, you'll learn the essential aspects of being a Scrum Master. By contrasting Scrum with traditional project management methods, you'll gain a deep understanding of why Scrum is a game-changer in today's dynamic business landscape. <br /><br />We also dive into the practical skills required to become a highly effective Scrum Master. Through hands-on practice and real-life scenarios, you'll develop your abilities to navigate challenges, foster collaboration, and drive successful project outcomes.  <br /><br />Although this course is beneficial for learners who are already project managers, it is not essential for them to have project management experience. <br /><br />With engaging video content and interactive exercises, you'll find yourself immersed in the world of Scrum. The course is designed to prepare you for the final exam, designed to simulate the certification exams you may encounter. Gain the confidence and experience you need to ace those tests and prove your expertise as a Scrum Master.<br /><br /></div><div><strong>What You'll Learn</strong></div><ul><li>Summarize the role of the Scrum Master to include a position description, benefits of the position, and key skills essential to success. </li><li>Compare and contrast the role of the Scrum Master to a traditional project manager. </li><li>Describe opportunities for Scrum Masters in the current work environment to include potential employment, job requirements, and salary ranges. </li><li>Define certification opportunities in Agile and Scrum to include Scrum Alliance CSM, PMI-ACP, and PMI-DASM. </li></ul><h1>Course Syllabus</h1><div>Module 1: The Role of the Scrum Master </div><ul><li>Video: Course Introduction: Scrum Master Profession</li><li>Reading: Course Overview: Scrum Master Profession</li><li>Reading: How to make the most from this course</li><li>Reading: Module Overview</li><li>Video: Scrum Master Role and Benefits </li><li>Video: Expert Viewpoints: What Does the Scrum Master Do? </li><li>Video: Scrum Master Skill Inventory </li><li>Video: Expert Viewpoints: Scrum Master Skills  </li><li>Video: A Day in the Life of a Scrum Master </li><li>Reading: Adaptive Methodologies </li><li>Lab: Case Study: The Role of a Scrum Master </li><li>Reading: Module 1 Summary: The Role of the Scrum Master </li><li>Practice Quiz: Module 1: The Role of the Scrum Master </li><li>Reading: Module 1 Glossary: The Role of the Scrum Master  </li><li>Graded Quiz: Module 1: The Role of the Scrum Master  </li></ul><div>Module 2: Scrum Master Opportunities </div><ul><li>Reading: Module 2 Overview and Key Takeaways</li><li>Video: Scrum Master and Traditional Project Manager Comparison  </li><li>Video: Expert Viewpoints: Scrum Master Opportunities </li><li>Video: Developing Scrum Master Skills </li><li>Video: Scrum Master Employment Opportunities </li><li>Video: Scrum Master Levels </li><li>Video: Expert Viewpoints: Why Become a Scrum Master </li><li>Lab: Potential Scrum Master Job Opportunities  </li><li>Reading: Module 2 Summary: Scrum Master Opportunities  </li><li>Practice Quiz: Module 2: Scrum Master Opportunities  </li><li>Reading: Module 2 Glossary: Scrum Master Opportunities  </li><li>Graded Quiz: Module 2: Scrum Master Opportunities  </li><li>Discussion Prompt: Are you now more or less motivated to pursue the role of Scrum Master? Why?</li></ul><div>Module 3: Scrum Master Job Overview and Opportunities </div><ul><li>Reading: Module 3 Overview </li><li>Reading: Module 1 and 2  Review and Key Takeaways </li><li>Video: Expert Viewpoints: Scrum Master Employment </li><li>Video: The Path to Certification PMI-ACP, PMI-DASM, and CSM  </li><li>Video: Expert Viewpoints: Scrum Master Job Environment </li><li>Reading: Module 3 Glossary: Scrum Master Job Overview and Opportunities   </li><li>Practice Assessment: Scrum Master Job Overview and Opportunities  </li><li>Reading: Course Glossary  </li><li>Graded Assessment: Scrum Master Job Overview and Opportunities  </li></ul><div>Module 5: Final Project  </div><ul><li>Reading: Final Project Overview </li><li>Lab: Project Scenario </li><li>Lab: Exercise 1: Perform a SWOT Analysis </li><li>Lab: Exercise 2: Take Action Based on SWOT Analysis </li><li>Final Project: Submission and Evaluation</li><li>Video: Expert Viewpoints: Advice for Aspiring Scrum Masters </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>This aspiring Scrum Master should possess some basic project management skills and experience. However, experience is not mandatory.  industries worldwide! </div>
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Introduction to Scrum Master Profession

Kick-start your Scrum Master journey and build an essential understanding of the role. 

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  <div>This course provides a comprehensive introduction to the transformative role of generative artificial intelligence (AI) in mobile app development. You will learn how to leverage generative AI across the app development lifecycle, including design, content generation, marketing, and user support. Through hands-on labs, engaging video lectures, and interactive quizzes, you will gain practical experience with leading AI tools like Claude AI, Copy.ai, and Apple Intelligence.<br /><br />The course begins with exploring generative AI concepts, focusing on its applications in app design, gaming, and user engagement. It also addresses the opportunities and challenges in using AI for personalized marketing and support. Then, you’ll dive deeper into advanced topics, such as machine learning models, embedded AI, and cloud-based development tools.<br /><br />By the end of the course, you will create a functional mobile app prototype, integrating generative AI features to enhance user experience. This course equips you with the skills to innovate in the rapidly evolving field of mobile technology, preparing you for roles in development, marketing, and beyond.</div><h1>Course Syllabus</h1><div><a href="https://gbc-word-edit.officeapps.live.com/we/wordeditorframe.aspx?ui=en-US&amp;rs=en-US&amp;IsLicensedUser=0&amp;WOPISrc=https%3A%2F%2Fapi.box.com%2Fwopi%2Ffiles%2F1593824934576#_Toc170240805"><strong>Module 1: Introduction to Generative AI for Mobile App</strong></a><strong> Development</strong> </div><ul><li>Video: Course Introduction </li><li>Reading: Course Overview </li><li>Reading: Helpful Tips for Course Completion </li><li>Video: Opportunities and challenges of Gen AI in mobile apps  </li><li>Video: Designing Mobile Apps using Gen AI  </li><li>Lab: Generate a quick prototype using Uizard </li><li>Video: Content creation using Gen AI for Mobile Apps </li><li>Lab: Generate a splash screen using Claude AI </li><li>Video: Transforming Gaming and AR/VR mobile apps with Gen AI </li><li>Demo Video: Exploring AR/VR mobile apps </li><li>Reading: Best practices for incorporating Gen AI in mobile design </li><li>Practice Quiz: Generative AI in Mobile App Development  </li><li>Video: Gen AI for mobile app Marketing  </li><li>Lab: Create a marketing campaign using copy.ai </li><li>Video: Gen AI for mobile app support </li><li>Lab: Creating a FAQ using Synthesia.io </li><li>Video: Gen AI for behavioral analysis and customization </li><li>Reading: Industry use cases of Gen AI in Mobile Apps </li><li>Practice Quiz: Generative AI for Mobile Marketing and Support </li><li>Reading: Summary and Highlights: Introduction to Generative AI for Mobile App Development </li><li>Graded Quiz: Introduction to Generative AI for Mobile App Development </li></ul><div><strong>Module 2:  Generative AI Tools for Mobile Development </strong></div><ul><li>Video: Generative AI for Cutting-Edge Android Apps </li><li>Demo video: Vertex AI Studio</li><li>Lab: Leverage Dialogflow for creating a Retail order assistant chatbot</li><li>Video: Elevate iOS Apps with Generative AI  </li><li>Demo video: Introducing Apple Intelligence </li><li>Reading: Developing with Apple Intelligence (Beta) </li><li>Practice Quiz: Generative AI Tools for Mobile Development </li><li>Video: Building intelligent Mobile apps with Machine Learning models  </li><li>Demo Video: AI Lifecycle platform - Clarifai</li><li>Video: AI powered IDEs for Mobile App development</li><li>Demo video: Exploring Project IDX </li><li>Lab: Creating a React Native app with Project IDX and Gemini AI for a Dynamic Form</li><li>Reading: Leveraging Custom AI models in cloud Mobile app Development </li><li>Practice Quiz: Advanced concepts for Gen AI in Mobile Apps</li><li>Reading: Summary and Highlights: Generative AI tools for Mobile development  </li><li>Graded Quiz: Generative AI tools for Mobile development </li></ul><div><a href="https://gbc-word-edit.officeapps.live.com/we/wordeditorframe.aspx?ui=en-US&amp;rs=en-US&amp;IsLicensedUser=0&amp;WOPISrc=https%3A%2F%2Fapi.box.com%2Fwopi%2Ffiles%2F1593824934576%22%20\l%20%22_Toc170240809"><strong>Module 3: Final Project, Final Exam and Course Wrap-Up</strong></a> </div><ul><li>Reading: Final Project Overview </li><li>Final Project: Smart Health App </li><li>Video: Course Wrap-Up </li><li>Reading: Useful Prompts for mobile app developers</li><li>Reading: Course Glossary </li><li>Reading: Congratulations and Next Steps </li><li>Reading: Thanks from the Course Team </li></ul><div><strong>What you'll learn</strong></div><ul><li>Understand generative AI concepts for mobile app development</li><li>Analyze opportunities and challenges in AI-driven app design and marketing</li><li>Demonstrate proficiency with AI tools like Vertex AI, Dialogflow, and Apple Intelligence</li><li>Build a mobile app prototype using generative AI tools</li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>Learners will need a desktop or laptop computer with a modern web browser and a reliable internet connection. </div>
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Generative AI: Turbocharge Mobile App Development

This course will explore the transformative potential of generative artificial intelligence in mobile app development. You will learn how to integrate AI tools and frameworks to enhance app design, marketing, and user engagement. As you progress through the course, you will gain insights into the opportunities and challenges of using generative AI in areas such as gaming, augmented reality, and virtual reality. You will also develop the skills to leverage advanced AI-driven solutions to build innovative mobile applications.

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  <div>Improve your front-end development skills by learning one of the most popular front-end frameworks, React, using ES6 in this hands-on course. This course is for you if you want to take the next step in your front-end development journey. </div><div> </div><div>In this course, you will build rich front-end applications with React and ES6 JavaScript specifications. You will learn JSX, a JavaScript extension that allows you to write HTML-like code embedded into your JavaScript code, easily allowing you to create React elements. You create these elements with React components and learn how to manipulate the components using the props (properties) and event handling. Then, you explore ways to pass data and states between components by understanding the  lifecycle. </div><div> </div><div>You will investigate advanced topics like “hooks,” allowing you to write readable, testable, and maintainable code more efficiently. You round out this class learning about the “Redux” library, which helps you write applications in different environments, allowing you to build complex, interactive React applications more easily.  </div><div>Throughout the course, you will build your portfolio by completing numerous hands-on labs to gain practical experience. At the end of the course, you pull together what you’ve learned with a final project by building a budgeting allocation application. <br /><br /></div><div>This course will help you succeed as a front-end or full-stack developer. It suits those in IT looking to step up in their careers or new graduates seeking to establish their client-side skills. To be successful in this course, you will need basic HTML, CSS, JavaScript, and Git skills. <br /><br /></div><h1>What You'll Learn</h1><ul><li>Write interactive applications using the React web framework. </li><li>Build React elements using JSX and React components. </li><li>Communicate and exchange data with external services using GET, POST, UPDATE, and DELETE requests. </li><li>Use props, states, and hooks to pass data and manipulate the states of React components. </li></ul><h1>Prerequisites</h1><div>Basic knowledge of HTML, CSS, JavaScript, and Git <br /><br /></div><h1>Course Syllabus</h1><div>Module 1: Building Rich Front-End Applications with React and ES6 </div><ul><li>Introduction to Front-End Frameworks and React </li><li>Insiders Viewpoints: Front-end Frameworks </li><li>Introduction to ES6 </li><li>Introduction to JSX </li><li>Hands-on Lab: Hello World with React </li><li>Introduction to Components </li><li>React Components - Props and Event Handling </li></ul><div><br />Module 2: React Components </div><ul><li>Introduction to States </li><li>Passing data and states between components </li><li>Components Lifecycle </li><li>Testing React Components </li><li>Hands-on Lab: React Todo List Application </li></ul><div><br />Module 3: Advanced React </div><ul><li>Hooks </li><li>Implementing Forms </li><li>Introduction to Redux </li><li>Essential Concepts of Redux  </li><li>Async with Redux </li><li>Binding Redux and Flow </li><li>Hands-on Lab: React Redux </li></ul><div><br />Module 4: Final Project </div><ul><li>Hands-on Lab: Budget Allocation Application </li><li>Optional Sample Project: The Shopping Application </li><li>Final Project: Budgeting Allocation Application </li></ul>
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Developing Front End Apps with React

Learn to build rich Front-End apps with React and ES6, connect components, write advanced components, and test them. Plus, a final project to add to your portfolio.

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  <div>This course is designed for enthusiasts and practitioners who share an interest in the rapidly advancing field of generative AI.<br /><br /></div><div>This course centers around the core concepts and generative AI models that form the building blocks of generative AI. You will delve into the concepts of deep learning and large language models (LLMs). You will learn about GANs, VAEs, transformers, and diffusion models – the fundamental components of generative AI.<br /><br /></div><div>You will learn about the concept of foundation models. You will gain insights into the capabilities of pre-trained models and platforms for AI application development. The course will also cover how foundation models utilize these platforms to generate text, images, and code. Additionally, participants will explore various generative AI platforms such as IBM watsonX and Hugging Face.<br /><br /></div><div>The course includes practical hands-on labs, offering participants the chance to delve into the applications of generative AI using the IBM Generative AI Classroom and platforms like IBM watsonX. Throughout the course, you'll have the opportunity to explore various models, including IBM Granite, OpenAI GPT, Google Flan, and Meta Llama. Additionally, expert practitioners will share insights into the capabilities, applications, and tools of generative AI. <br /><br /><strong>What you'll learn</strong></div><ul><li>Describe the fundamental concepts of generative AI.</li><li>Explore the building blocks of generative AI, including GANs, VAEs, transformers, and diffusion models.</li><li>Explain the concept of foundation models in generative AI.</li><li>Explore the ability of foundation models to generate text, images, and code using pre-trained models.</li><li>Describe the features, capabilities, and applications of different generative AI platforms, including IBM Watson and Hugging Face.</li></ul><h1>Course Syllabus</h1><div><strong>Module 1: Models for Generative AI</strong></div><ul><li>Video: Course Introduction</li><li>Reading: Course Overview</li><li>Reading: Program Overview</li><li>Reading: Helpful Tips for Course Completion</li><li>Video: Deep Learning and Large Language Models</li><li>Video: Generative AI Models</li><li>Video: Foundation Models</li><li>Labs: Generative AI Foundation Models</li><li>Expert Viewpoints Video: Advancing Generative AI Capabilities</li><li>Expert Viewpoints Video: Exploring Generative AI Capabilities of IBM Watsonx</li><li>Reading: Module Summary</li><li>Practice Quiz: Core Concepts and Models of Generative AI</li><li>Discussion Prompt: Working with Foundation Models</li><li>Reading: IBM Granite Foundation Models</li><li>Graded Quiz: Models for Generative AI</li></ul><div><strong>Module 2: Platforms for Generative AI</strong></div><ul><li>Video: Pre-trained Models: Text-to-Text Generation</li><li>Lab: Develop AI Applications with the Foundation Models</li><li>Video: Pre-trained Models: Text-to-Image Generation</li><li>Video: Pre-trained Models: Text-to-Code Generation</li><li>Lab: Develop AI Applications for Code Generation</li><li>Video: IBM Watsonx.ai</li><li>Reading: IBM Watsonx. data and Watsonx. governance</li><li>Video: Hugging Face</li><li>Expert Viewpoints Video: Fostering Innovation through Hugging Face</li><li>Reading: Module Summary</li><li>Practice Quiz: Pre-trained Models and Platforms for AI Applications Development</li><li>Graded Quiz: Platforms for Generative AI</li></ul><div><strong>Module 3: Course Quiz and Final Project</strong></div><ul><li>Reading: Module Introduction and Learning Objectives</li><li>Glossary - Generative AI: Foundation Models and Platforms</li><li>Final Project: Working with IBM Granite Foundation Models</li><li>Final Exam: Graded Quiz: Generative AI: Foundation Models and Platforms</li></ul><div><strong>Course Wrap-Up</strong></div><ul><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>This course is for anyone wanting to learn and get familiar with the concepts of generative AI. No prior knowledge is required for this course. Note that this platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.</div>
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Models and Platforms for Generative AI

Understanding the core concepts and foundational models of generative AI gives you the footing that you need in the rapidly developing field of generative AI, which is transforming our world. Along with deep learning and LLMs, you'll explore GANs, VAEs, transformers, and diffusion models, which are the building blocks of generative AI. This course is for all enthusiasts and practitioners with an interest in learning what generative AI is and how it works.

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  <div>In today's rapidly evolving AI landscape, AI systems often produce inconsistent, unpredictable outputs when multiple agents need to communicate with each other. When one agent's output becomes another's input, the lack of structure leads to cascading errors, misinterpretations, and unusable results. This guided project addresses this fundamental challenge by showing you how to enforce structured data exchanges between AI agents.<br /><br /></div><div>You'll learn how to ensure consistent, structured outputs from AI agents - a common challenge in AI development. Working with a research report scenario, you'll build a system that transforms unstructured AI responses into professional documents.<br /><br /></div><div>You'll design hierarchical data models with Pydantic as blueprints for your AI outputs, then create specialized CrewAI agents with distinct roles: a researcher, an analyst, and a formatter. You'll coordinate these agents in a workflow where each agent's output becomes structured input for the next, using data models to eliminate inconsistencies. By the end, you'll have a reusable framework for generating well-structured reports on any subject, automating what would normally take days of manual work into a process that produces professional results within hours.</div><div><br /><br /></div><h1>What You'll Learn</h1><div>By completing this lab, you will:</div><ul><li>Create structured data models using Pydantic to enforce consistency in LLM outputs</li><li>Design specialized AI agents with specific roles using CrewAI</li><li>Implement multi-stage workflows that transform research into organized documents</li><li>Build hierarchical data structures for proper validation and formatting</li><li>Generate professional reports with consistent formatting and citations</li><li>Apply data validation techniques to prevent incomplete LLM outputs</li></ul><div><br /></div><h1>Who should enroll</h1><div>This project is perfect for:</div><ul><li>AI developers looking to build more reliable agent-based systems</li><li>Data scientists who need to generate consistent reports from AI research</li><li>Software engineers interested in structured data handling with AI</li><li>Business analysts wanting to automate research and reporting workflows</li></ul><div><br /></div><h1>What you'll need</h1><div>Before beginning this guided project, you should have:</div><ul><li>Basic understanding of Python programming</li><li>Access to a modern web browser for the IBM Skills Network Labs environment</li></ul><div><br /></div><h1>Why enroll</h1><div>The ability to create AI systems that produce structured, consistent outputs is increasingly critical as organizations move AI from experimentation to production. By learning to enforce data models and orchestrate specialized agents, you'll gain valuable skills for building reliable AI applications that deliver professional results in research, content generation, and data analysis.</div><div><br /></div>
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Structuring LLM Outputs with CrewAI and Pydantic for Agents

Transform chaotic LLM outputs into consistent, professional documents by implementing structured data models with Pydantic and multi-agent workflows in CrewAI. This project teaches you to design specialized AI agents that collaborate to research topics and generate formatted reports, ensuring reliable outputs regardless of the subject matter. By creating a hierarchical data architecture that enforces validation and formatting rules, you'll learn to build AI systems that consistently deliver client-ready documents with proper structure, citations, and visual formatting.

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  <div>What separates sophisticated AI from basic models? The ability to critique and improve its own work. In this guided project, you'll learn how to implement reflection agents using LangGraph—AI systems that evaluate their outputs, identify weaknesses, and iteratively enhance their responses without human intervention.<br /><br /></div><div>You'll construct a complete workflow that mimics the human creative process: generate content, step back to evaluate it, then revise based on deliberate feedback. Working with a practical tweet generation example, you'll witness how reflection transforms basic AI responses into polished, engaging content through multiple refinement cycles.<br /><br /></div><div>This project reveals the architecture behind self-improving agentic AI systems, giving you hands-on experience with one of the most important advancements in modern AI development—the ability to implement artificial "System 2" thinking where deliberate reflection leads to superior results.<br /><br /></div><h1><strong>What you'll learn</strong></h1><div>After completing this project, you will be able to:</div><ul><li><strong>Design reflection workflows</strong> with LangGraph that enable AI to critique and improve its own outputs</li><li><strong>Implement generation, evaluation, and refinement stages</strong> in a connected agentic system</li><li><strong>Create feedback mechanisms</strong> that allow AI to objectively assess its content quality</li><li><strong>Build conditional logic</strong> to control when reflection cycles should continue or end</li><li><strong>Apply reflection principles</strong> to various AI applications beyond the project examples</li></ul><div><br /></div><h1><strong>Who this project is for</strong></h1><div>This project is perfect for:</div><ul><li><strong>Software developers</strong> seeking to build more sophisticated and reliable systems</li><li><strong>Data scientists</strong> working with large language models</li><li><strong>ML engineers</strong> interested in self-improving agentic AI applications</li><li><strong>Content creators</strong> looking to leverage AI with refinement capabilities</li></ul><div><br /></div><h1><strong>What you'll need</strong></h1><div>Before beginning this guided project, you should have:</div><ul><li>Basic understanding of Python programming</li><li>Familiarity with LLM concepts and applications</li><li>Access to a modern web browser for the IBM Skills Network Labs environment</li></ul><div><br /></div><h1><strong>Why enroll</strong></h1><div>By the end of this project, you'll possess the skills to build AI systems that think twice—generating content and then critically reviewing it before delivering final results. This reflection capability is the key difference between basic AI applications and truly intelligent systems. Whether you're developing content generators, virtual assistants, or analytical tools, incorporating reflection mechanisms with LangGraph dramatically improves output quality and reliability. Join this project to learn how to transform one-shot AI responses into thoughtful, refined content through the power of self-critique and iteration.</div>
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Make Your AI Agents Smarter with Reflection in LangGraph

Build AI Agents that reason, critique their own work, and iteratively improve their responses just like a human. This project teaches you to build a tweet optimization workflow where content improves with each iteration, just like human editing. In just 45 minutes, master the principles of reflection agents that can recognize their own limitations and actively improve their responses—an essential skill for creating more intelligent, agentic AI systems.

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  <div>Language models become dramatically more powerful when they can interact with external services and data. In this hands-on guided project, you'll learn how to build this critical bridge between AI and the digital world through custom tool development.<br /><br /></div><div>Using YouTube content analysis as our example, you'll create specialized tools that enable language models to perform actions they couldn't accomplish alone. You'll build functions that extract video IDs, fetch transcripts, search for content, retrieve metadata, and access trending videos—giving your AI assistant comprehensive media analysis capabilities.<br /><br /></div><div>The project focuses on manual tool calling—controlling how LLMs select and execute tools. You'll learn how language models decide which tools to use, how to extract their requests, execute the appropriate functions, and return results in the expected format. This understanding is essential for debugging, customization, and building reliable AI systems.</div><div><br /></div><h1><strong>What you'll learn</strong></h1><div>After completing this project, you will be able to:</div><ul><li>Design custom tools with proper documentation, type hints, and error handling</li><li>Understand how LLMs select appropriate tools based on user requests</li><li>Extract tool call information from LLM responses</li><li>Execute tool calls manually with correct parameter formatting</li><li>Structure and manage conversations that include multiple tool calls</li><li>Build organized tool mappings for complex applications</li><li>Implement chains that handle multi-step tool execution workflows</li></ul><div><br /></div><h1><strong>Who should enroll</strong></h1><div>This project is perfect for:</div><ul><li>AI developers seeking to extend language model capabilities</li><li>Software engineers building LLM-powered applications</li><li>Data scientists interested in programmatic content analysis</li><li>Technology professionals implementing practical AI solutions</li><li>Content creators looking to automate YouTube data collection</li></ul><div><br /></div><h1><strong>What you'll need</strong></h1><div>Before beginning this guided project, you should have:</div><ul><li>Basic understanding of Python programming</li><li>Familiarity with language models and their capabilities</li><li>Access to a modern web browser for the IBM Skills Network Labs environment</li><li>Optional: Prior experience with LangChain is helpful but not required</li></ul><div><br /></div><h1><strong>Why enroll</strong></h1><div>By the end of this project, you'll understand the mechanics behind today's most advanced AI assistants. The ability to create custom tools and control their execution is what separates basic chatbots from powerful AI systems that can search databases, analyze content, and interact with external services.<br /><br /></div><div>As organizations increasingly adopt AI solutions, the demand for developers who understand tool integration continues to grow. Whether you're building research assistants, content analysis tools, or customer service agents, mastering custom tool development will enable you to create AI applications that deliver real-world value by combining the reasoning capabilities of language models with access to specialized functions and external data sources.</div>
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Build and Execute Your Own Tools for LLMs

Connect language models to the digital world by building tools that serve as their hands and eyes. This project shows you how to create specialized functions that let LLMs interact with YouTube videos and external services. You'll master the process of designing tools and controlling their execution through manual tool calling. Learn to build functions that extract video IDs, fetch transcripts, and analyze trending content. In just 1 hour, gain the expertise to create AI systems that search and analyze video content and understand the mechanics behind today's most powerful AI assistants.

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  <div>Learn to implement agentic workflows with LangGraph and discover how to build intelligent AI agents capable of making dynamic, context-aware decisions. This guided project will walk you through the core components of LangGraph, including nodes, states, edges, and conditional edges, to help you create flexible and autonomous systems. You’ll learn how to design and implement sophisticated workflows, such as user authentication systems, that adapt to different scenarios based on success, failure, or conditional logic.  <br /><br /></div><div>Through hands-on exercises, you’ll gain a deep understanding of how each component works and how to connect them to design seamless decision-making workflows. Whether you’re managing state transitions, setting up dynamic conditions, or creating failure-handling mechanisms, this project will empower you to create advanced AI systems with high levels of flexibility and autonomy.<br /><br /></div><div><strong>What You’ll Learn<br /></strong><br /></div><ul><li><strong>LangGraph Essentials</strong>: Understand the key components—nodes, states, edges, and conditional edges—that power decision-making in AI systems.</li><li><strong>Dynamic Workflows</strong>: Learn how to build user authentication systems and other workflows with flexible decision logic, tailored to different user states and conditions.</li><li><strong>State-driven Logic</strong>: Explore how to manage state transitions and conditional logic to ensure smooth execution of tasks, handling both success and failure scenarios.</li><li><strong>Building Autonomous Systems</strong>: Develop skills to create autonomous agents that adapt based on real-time inputs, making intelligent decisions that reflect changing conditions.</li></ul><div><br /></div><div><strong>Why It Matters</strong><br /> LangGraph represents a powerful framework for building AI agents with robust decision-making capabilities. By mastering LangGraph, you will:</div><ul><li><strong>Design Flexible Workflows</strong>: Create intelligent systems that react and adapt to user input and evolving scenarios.</li><li><strong>Enable Autonomous Systems</strong>: Build systems capable of acting independently, managing success, failure, and conditions based on real-time data.</li><li><strong>Enhance Decision-making</strong>: Make data-driven decisions by integrating dynamic decision logic and understanding the flow of information between nodes, states, and edges.</li></ul><div><br /></div><div><strong>Who Should Register</strong><br /> This guided project is ideal for:</div><ul><li><strong>Developers</strong>: Those interested in building intelligent AI systems that require dynamic decision-making.</li><li><strong>AI Enthusiasts</strong>: Anyone looking to deepen their understanding of AI workflows and how to build flexible, autonomous systems.</li><li><strong>Software Engineers</strong>: Developers working on user authentication or similar workflows that require decision-based logic and state management.</li></ul><div><br /></div><div><strong>What You’ll Need</strong><br /> Before starting this project, ensure you have the following:</div><ul><li><strong>Basic Programming Knowledge</strong>: Familiarity with basic programming concepts will be helpful, especially in Python.</li><li><strong>Understanding of Decision-making</strong>: While prior experience with decision-making workflows is a plus, it’s not required.</li><li><strong>A Computer with a Modern Browser</strong>: Chrome, Edge, Firefox, or Safari to run and interact with the project tools.</li></ul><div><br /></div><div><strong>What You’ll Build</strong><br /> By the end of this guided project, you will have:</div><ul><li><strong>Mastered LangGraph</strong>: A deep understanding of how to build and connect the essential components of LangGraph for creating AI workflows.</li><li><strong>Built Dynamic Workflows</strong>: Created user authentication workflows that handle success and failure scenarios and adapt to changing conditions.</li><li><strong>Developed Autonomous Systems</strong>: Gained hands-on experience in designing systems that make real-time decisions based on data inputs and conditions.</li></ul><div><br /></div><div>Embark on this exciting journey with LangGraph to build intelligent AI agents that think, adapt, and make decisions in real-time. Elevate your AI development skills and start building dynamic decision-making systems today!</div>
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LangGraph 101: Learn to Build Agentic Workflows

Create agentic workflows with LangGraph by learning key components like nodes, states, edges, and conditional edges through a user authentication use case. Learn how to design AI workflows that adapt to real-time inputs and manage state-driven logic effectively. You’ll explore how to code success and failure scenarios, build dynamic decision-making systems, and create workflows tailored to diverse conditions. Perfect for developers and AI enthusiasts, this project focuses on equipping you with practical skills to design flexible, autonomous AI systems for real-world applications. 

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  <div><strong>Imagine having a data analyst at your fingertips</strong> who understands natural language and can perform complex analyses without requiring you to write a single line of code. What if you could simply ask questions about your data and receive immediate insights, visualizations, and even predictive models? This is the power of combining <strong>language models with specialized data science tools</strong>.</div><div><br />In this hands-on lab, you'll build a conversational AI system that makes data science accessible to everyone in your organization—from marketing specialists analyzing customer segments to executives exploring sales trends, all without technical barriers.<br /><br /></div><h1>Project Overview</h1><div>This lab teaches you to create an intelligent assistant that handles the entire data workflow through natural language requests:</div><div>1️⃣ <strong>Data Discovery &amp; Exploration</strong> - Automatically identify available datasets, summarize their structure, and provide quick statistics<br />2️⃣ <strong>Analysis &amp; Visualization</strong> - Transform requests like "Show the relationship between customer age and spending" into visualizations<br />3️⃣ <strong>Predictive Modeling</strong> - Train and evaluate machine learning models through conversation<br />4️⃣ <strong>Context Management</strong> - Maintain conversation history to enable follow-up questions and complex analyses</div><div><br />By connecting specialized LangChain tools through an agent executor, you'll create a seamless experience where users can move from raw data to actionable insights using only conversation.<br /><br /></div><h1>What You'll Learn</h1><div>By completing this lab, you will:</div><ul><li>Design effective <strong>tool-based agents</strong> that reason about when to use specific data functions</li><li>Build a collection of <strong>specialized tools</strong> for different data science tasks</li><li>Create <strong>multi-step workflows</strong> that maintain context across analyses</li><li>Implement <strong>natural language interfaces</strong> to technical data processes</li><li>Learn how to handle ambiguous user requests with <strong>clarification techniques</strong></li></ul><div><br /></div><h1><strong>Who Should Do This Lab</strong></h1><div>This project is ideal for:</div><ul><li><strong>Developers</strong> looking to build practical AI applications with immediate business value</li><li><strong>Data scientists</strong> wanting to make their expertise available to non-technical colleagues</li><li><strong>Technical leaders</strong> exploring how AI can democratize data-driven decision making</li><li><strong>AI enthusiasts</strong> interested in creating assistants that solve real-world problems</li></ul><div>No advanced ML expertise required—basic Python knowledge and curiosity about AI applications are all you need.<br /><br /></div><h1>What You Need</h1><div>✅ <strong>A browser</strong> to access the lab environment<br />✅ <strong>Basic Python knowledge</strong> (understanding functions and data structures)<br />✅ <strong>Familiarity with data concepts</strong> (CSV files, simple statistics, basic ML terms)</div><div><br />By the end of this project, you'll have built an AI assistant that transforms how people interact with data—enabling anyone to ask questions and receive insights without writing code.</div>
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Build a LangChain-based AI-powered Data Analysis Assistant

Learn LangChain tools and agents while developing a data science assistant to automate tasks from exploring CSVs to training ML models. This project guides you through creating a natural language interface that makes data analysis accessible to non-technical users.  Build specialized tools that handle everything from listing available datasets to generating statistics and evaluating machine learning models. Learn how AI agents interpret user requests, select appropriate tools, and execute complex data workflows through conversation. 

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  <div>As businesses are increasingly moving toward data-driven decision-making, the ability to derive meaningful insights from raw data is becoming all the more 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. You will be working on a project simulating a real-life scenario with actual datasets. The project would involve carrying 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.  <br /><br />Throughout the project, you will demonstrate your proficiency in tools such as Jupyter Notebooks, SQL, and Relational Databases Management System (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 />Your final deliverable will be a comprehensive data analysis report that includes an executive summary, detailed insights, and a conclusion for organizational stakeholders. <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>Course 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>Welcome to the Course</strong></div><ul><li>Video: Course Introduction</li><li>Reading: General Information: About the course</li><li>Reading: Course Learning Objectives and Course Syllabus</li><li>Video: Project Overview</li><li>Reading: Project Scenario</li><li>Reading: Grading Scheme</li></ul><div><strong>Module 1: Data Collection<br />Collecting Data Using APIs</strong></div><ul><li>Reading: Module 1 Introduction and Learning Objectives</li><li>(Optional) Lab 1: Review Of Accessing APIs</li><li>Reading: API Access Details </li><li>Lab 2:  Collecting Data Using APIs</li><li>Checklist: Collecting Data Using APIs</li></ul><div><strong>Collecting Data Using Webscraping</strong></div><ul><li>Lab 3: Review Of Web Scraping</li><li>Lab 4: Collecting Data Using Web Scraping</li><li>Checklist: Collecting Data Using Webscraping</li></ul><div><strong>Exploring Data</strong></div><ul><li>Reading: About the Dataset</li><li>Lab 5: Exploring the Dataset</li><li>Checklist:  Exploring Data</li><li>Graded Quiz: Data Collection</li></ul><div><strong>Module 2: Data Wrangling</strong></div><div><strong>Finding Duplicates</strong></div><ul><li>Reading: Module 2 Introduction and Learning Objectives</li><li>Reading: Assignment Overview</li><li>Lab 6: Finding Duplicates</li><li>Checklist: Finding Duplicates</li></ul><div><strong>Removing Duplicates</strong></div><ul><li>Lab 7: Removing Duplicates</li><li>Checklist: Removing Duplicates</li></ul><div><strong>Finding Missing Values</strong></div><ul><li>Lab 8: Finding Missing Values</li><li>Checklist: Finding Missing Values</li></ul><div><strong>Imputing Missing Values</strong></div><ul><li>Lab 9: Impute Missing Values</li><li>Checklist: Imputing Missing Values</li></ul><div><strong>Normalizing Data</strong></div><ul><li>Lab 10: Normalizing Data</li><li>Checklist: Normalizing Data</li></ul><div><strong>Assignment Overview</strong></div><ul><li>Lab 11: Data Wrangling</li><li>Checklist: Data Wrangling</li><li>Graded Quiz: Data Wrangling</li></ul><div><strong>Module 3: Exploratory Data Analysis</strong></div><div><strong>Assignment Overview</strong></div><ul><li>Reading: Module 2 Introduction and Learning Objectives</li><li>Reading: Assignment Overview</li><li>Lab 12: Exploratory Data Analysis</li><li>Checklist: Exploratory Data Analysis</li></ul><div><strong>Analyzing the data distribution</strong></div><ul><li>Lab 13: Finding How The Data Is Distributed</li><li>Checklist: Analyzing the data distribution</li></ul><div><strong>Handling Outliers</strong></div><ul><li>Lab 14: Finding Outliers</li><li>Checklist: Handling Outliers</li></ul><div><strong>Correlation</strong></div><ul><li>Lab 15: Finding Correlation</li><li>Checklist: Correlation</li><li>Graded Quiz: Exploratory Data Analysis</li></ul><div><strong>Module 4: Data Visualization</strong></div><div><strong>Assignment Overview</strong></div><ul><li>Reading: Module 4 Introduction and Learning Objectives</li><li>ReadIng: Assignment Overview</li><li>Lab 16: Data Visualization</li><li>Checklist: Data Visualization</li></ul><div><strong>Visualizing Distribution Of Data</strong></div><ul><li>Lab 17: Histograms</li><li>Lab 18: Box Plots</li><li>Checklist: Visualizing Distribution Of Data</li></ul><div><strong>Visualizing Relationship</strong></div><ul><li>Lab 19: Scatter Plot</li><li>Lab 20: Bubble Plots</li><li>Checklist: Visualizing Relationship</li></ul><div><strong>Visualizing Composition of Data</strong></div><ul><li>Lab 21: Pie Charts</li><li>Lab 22: Stacked Charts</li><li>Checklist: Visualizing Composition of Data</li></ul><div><strong>Visualizing Comparison of Data</strong></div><ul><li>Lab 23: Line Charts</li><li>Lab 24: Bar Charts</li><li>Checklist:  Visualizing Comparison of Data</li><li>Graded Quiz: Data Visualization</li></ul><div><strong>Module 5: Building A Dashboard</strong></div><div><strong>Assignment Overview</strong></div><ul><li>Reading: Module 5 Introduction and Learning Objectives</li><li>Reading: Assignment Overview</li></ul><div><strong>Dashboards</strong></div><ul><li>Lab 25: Option A - Building A Dashboard With IBM Cognos Analytics</li><li>Lab 26: Option B - Building A Dashboard With Google Looker Studio</li><li>Checklist: Dashboards</li><li>Graded Quiz: Building a Dashboard</li></ul><div><strong>Module 6: Final Assignment: Present Your Findings</strong></div><div><strong>How To Present Your Findings</strong></div><ul><li>Reading: Module 6 Introduction and Learning Objectives</li><li>Video: Elements Of A Successful Data Findings Report</li><li>Lab: Structure Of A Report</li><li>Video: Best Practices For Presenting Your Findings</li><li>(Optional)  Lab 27: Getting Started With PowerPoint For The Web</li><li>(Optional)  Lab 28: Basics of PowerPoint</li><li>(Optional) Lab 29: Save your PowerPoint Presentation as PDF</li></ul><div><strong>Final Presentation</strong></div><ul><li>Reading: Assignment Overview</li><li>Exercise: Preparing Your Presentation</li><li>Peer-graded Assignment</li></ul><div><strong>Course Wrap Up</strong></div><ul><li>Congratulations and Next Steps</li><li>Thanks from the Course Team</li><li>Copyrights and Trademark</li></ul><div><br /></div><h1>Recommended Skills Prior to Taking this Course</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. I</div>
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IBM Data Analytics and Visualization Capstone Project

Showcase your data analysis skills to prospective employers by carrying out common tasks performed by data analysts on a data set simulating a real-life business scenario and creating a comprehensive data analysis report.  

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  <div>"A picture is worth a thousand words". We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large data sets. Data visualization plays an essential role in the representation of both small and large scale data.</div><div>One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an approachable and stimulating way. Learning how to leverage a software tool to visualize data will also enable you to extract information, better understand the data, and make more effective decisions.</div><div>The main goal of this course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we will be using the open source language R. </div>
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Data visualization with R

Data visualization is the presentation of data with graphics. It's a way to summarize your findings and display it in a form that facilitates interpretation and can help in identifying patterns or trends. In this course you will learn how to create beautiful graphics and charts, customizing the look and feel of them as you wish.

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  <div>​Selecting the appropriate AI model for your tasks can be challenging, especially when deciding between standard and reasoning modes. Anthropic's Claude 3.7 Sonnet offers a unique solution with its hybrid reasoning capabilities, allowing users to tailor the model's behavior to specific needs.<br /><br />By integrating Claude into your development environment, you can create a range of applications, including <strong>creative content generation, coding assistance, and analytical tasks</strong>. Claude's adaptability ensures that responses are both contextually appropriate and efficient, enhancing user engagement across various platforms.</div><div><br />Furthermore, Claude's "extended thinking" mode enables detailed, step-by-step reasoning, making it particularly effective for tasks requiring in-depth analysis. This feature enhances Claude's utility in solving complex problems and performing intricate tasks, providing users with a versatile and powerful AI tool.</div><div><br />By the end of this lab, you will have a solid understanding of how to effectively use Claude's API, design prompts that maximize AI performance, and choose between reasoning and non-reasoning models based on task requirements.</div><h1>A Look at the Project Ahead</h1><div>After completing this lab you will be able to:</div><ul><li>Learn how to set up and use Anthropic's Claude API. ​</li><li>Develop skills in prompt engineering to elicit high-quality outputs from AI models.</li><li>Understand the differences between Claude's reasoning capabilities and standard models, and learn to select the appropriate model based on specific task requirements. ​</li><li>Understand how to adjust Claude's "thinking budget" and other parameters to balance response quality and processing efficiency according to your application's needs.</li></ul><h1>What You'll Need</h1><div>To get started with this guided project, you should have:</div><ul><li>Access to modern web browsers like Chrome, Edge, Firefox, Internet Explorer, or Safari.</li></ul>
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Explore Reasoning v/s Non-Reasoning Models with Claude API

Use Anthropic's API to experiment with Claude Sonnet's reasoning and non-reasoning capabilities. By utilizing Anthropic's API, you can tailor Claude's behavior to align with your specific needs, whether it's for generating creative content, solving complex coding challenges, or engaging in detailed analytical tasks. This adaptability makes Claude a powerful tool for a wide range of applications, enhancing both productivity and the quality of AI-generated outputs. By experimenting with both models, we aim to identify key differences and determine which model is best suited for specific tasks.

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  <div>Imagine a world where patients can receive <strong>instant, AI-powered medical assistance</strong> anytime, anywhere. Whether it’s a late-night health concern, a follow-up on medication, or general health advice, <strong>AutoMed</strong> steps in as your intelligent virtual healthcare assistant. No more long waiting times, delayed responses, or unreliable online searches—AutoMed provides <strong>real-time, accurate, and personalized</strong> medical support tailored to each individual’s needs.</div><div><br />AutoMed is not just an ordinary chatbot—it’s a <strong>multi-agent AI system powered by AG2 (AutoGen)</strong>, designed to <strong>simulate expert medical consultation</strong> through intelligent collaboration. Instead of relying on a single AI model, <strong>AutoMed orchestrates multiple specialized agents</strong>, each dedicated to a specific task, ensuring <strong>comprehensive, accurate, and real-time</strong> medical guidance. With its <strong>adaptive intelligence and multi-agent communication</strong>, AutoMed delivers <strong>human-like, context-aware conversations</strong> that go beyond basic symptom checkers.<br /><br /><strong>Disclaimer</strong>: This guided project is designed to introduce learners to AG2 (AutoGen). The medical advice provided should not be considered a substitute for professional medical consultation, diagnosis, or treatment. Always seek guidance from a qualified healthcare professional.</div><h1>A Look at the Project Ahead</h1><div>After completing this lab you will be able to:</div><ul><li>Learn how <strong>AG2</strong> enables <strong>multi-agent AI systems</strong> for complex workflows.</li><li>Explore how <strong>AG2 integrates with LLMs</strong> like GPT-4 for dynamic AI-driven conversations.</li><li>Implement <strong>agent-to-agent communication</strong> for <strong>intelligent medical decision-making</strong>.</li><li>Develop <strong>multiple AI agents</strong> that interact and collaborate to handle different healthcare tasks.</li></ul><h1>What You'll Need</h1><div>The IBM Skills Network Labs environment comes with many things pre-installed (e.g. Docker) to save the  hassle of setting everything up. This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari.</div>
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Build MultiAgent Chatbot with AG2 (AutoGen) for Healthcare

Learn to build AI agents using AG2 (AutoGen) multi-agent framework. In this project you will create AutoMed Multi LLM Agent for health treatment recommendations. It leverages Large Language Models (LLMs) to analyze user symptoms, suggest treatments, and access medical databases for accurate insights. You will build multiple AI agents that interact with each other, collaborate on complex medical queries, and automate healthcare assistance. These agents can coordinate tasks, fetch real-time medical data, and provide context-aware responses for better decision-making.

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

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

<div>Imagine you're applying for a mortgage to buy your dream home. After weeks of gathering documents and waiting anxiously, you receive a simple rejection letter with no explanation. You're left wondering: <strong>What went wrong? Was it your income, your down payment, or something in your credit history?</strong> Without clear reasons, you have no idea what to fix before trying again. This is where <strong>Explainable AI (XAI)</strong> makes a difference—transforming vague rejections into specific, actionable feedback that gives you a clear path forward.<br><br>As AI becomes increasingly embedded in critical decision-making processes across industries, the ability to understand why and how models reach specific conclusions is essential for building trust, ensuring fairness, and meeting regulatory requirements.<br><br>Here's a glimpse into the <strong>IBM's AI Explainability 360 (AIX360)</strong> decision framework you'll master in this learning path—helping you navigate which explainability techniques work best for different scenarios<br><br><figure data-trix-attachment="{&quot;contentType&quot;:&quot;image/png&quot;,&quot;filename&quot;:&quot;methods-choice.png&quot;,&quot;filesize&quot;:164704,&quot;height&quot;:1362,&quot;sgid&quot;:&quot;eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJalJuYVdRNkx5OXdiM0owWVd4ekwwRmpkR2wyWlZOMGIzSmhaMlU2T2tKc2IySXZOemdfWlhod2FYSmxjMTlwYmdZNkJrVlUiLCJleHAiOm51bGwsInB1ciI6ImF0dGFjaGFibGUifX0=--c4a675b2462a5482e72c3ee025f577e1a73da307&quot;,&quot;url&quot;:&quot;https://latest-catalog.skillsnetwork.site/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaHBVdz09IiwiZXhwIjpudWxsLCJwdXIiOiJibG9iX2lkIn19--a99a9c540b33c9edec72f28d5d853169efc695ae/methods-choice.png&quot;,&quot;width&quot;:2048}" data-trix-content-type="image/png" data-trix-attributes="{&quot;presentation&quot;:&quot;gallery&quot;}" class="attachment attachment--preview attachment--png"><img src="https://latest-catalog.skillsnetwork.site/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaHBVdz09IiwiZXhwIjpudWxsLCJwdXIiOiJibG9iX2lkIn19--a99a9c540b33c9edec72f28d5d853169efc695ae/methods-choice.png" width="2048" height="1362"><figcaption class="attachment__caption"><span class="attachment__name">methods-choice.png</span> <span class="attachment__size">160.84 KB</span></figcaption></figure><br><br>The XAI methodology flowchart illustrates how to choose the right explainability approach based on your specific needs. Don't worry if you find this confusing at first—you'll understand these concepts step by step as you progress through the learning path. The framework starts with a fundamental decision: Do you need to understand the data or the model? Then it guides you through choosing between sample-based or feature-based explanations, local explanations (for individual predictions) or global explanations (for the entire model). The flowchart shows various techniques available in the AIX360 toolkit, including ProtoDash for prototype-based explanations, SHAP for feature importance, TED for user-friendly explanations, and BRCG for directly interpretable models.<br><br>By completing this learning path, you'll gain practical experience with diverse explainable AI techniques using <strong>IBM's AI Explainability 360 (AIX360) </strong>open-source toolkit. Through four guided projects across different domains, you'll develop skills to create AI systems that are not only accurate but also transparent and trustworthy—expertise that's increasingly valuable as organizations face growing demands for responsible AI from regulators, customers, and internal stakeholders.</div><div><br></div><ul><li><strong>Employee Retention Analysis</strong> - Identify factors influencing whether employees stay or leave</li><li><strong>Credit Approval Decisions</strong> - Create transparent explanations for financial decisions</li><li><strong>Housing Market Predictions </strong>- Build interpretable rule-based models for property valuations</li><li><strong>Student Performance Prediction</strong> - Generate representative profiles explaining academic outcomes</li></ul><div><br>This expertise is increasingly valuable as organizations face growing demands for responsible AI. Whether enhancing existing models or building new systems with explainability by design, this learning path provides the foundation to implement XAI effectively in your work.</div>

Master Explainable AI using AIX360

Learn practical techniques on how to make AI systems transparent, explainable, and trustworthy with IBM's AIX360 library. 

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  <div>This comprehensive course is designed for aspiring and current Product Owners aiming to deepen their understanding of Scrum practices and enhance their Agile expertise. It discusses the Product Owner's role, collaborating effectively with project sponsors, and creating key Scrum artifacts. Through hands-on activities, you will develop initial artifacts, such as user stories and product backlogs, while mastering the essential Agile ceremonies, including kickoffs and sprint planning.<br /><br /></div><div>The course then explores the ways Product Owners manage sprints, prioritize product backlogs, and resolve roadblocks. You will learn about the creation of Sprint goals and acceptance criteria, as well as how to ensure value delivery and stakeholder alignment. You’ll learn about Agile retrospectives and continuous improvement strategies. You’ll also explore the ways Product Owners use tools and methods to balance Agile principles with practical documentation, including innovations such as AI in product management.<br /><br /></div><div>This course includes hands-on projects that allow you to apply your learning to a real-world scenario. You will calculate performance metrics, complete a sprint retrospective, and present findings to stakeholders. </div><div>Additionally, you will be guided through building an Agile artifact portfolio, preparing for certification, and optimizing your job search strategies.</div><div><br /></div><h1>Course Syllabus</h1><div><strong>Module 1: Product Owner and Scrum: The Kickoff</strong>: </div><ul><li><strong>Welcome to the Course</strong></li><li> <strong>Product Owner Role Fundamentals</strong></li><li> <strong>Module Summary and Assessment</strong></li></ul><div><strong>Module 2: Delivering Value to the Customer</strong></div><ul><li>Managing Sprints</li><li>Overcoming Issues and Roadblocks</li><li>Module Summary and Assessment</li></ul><div><strong>Module 3: Continuous Improvement</strong></div><ul><li>MVP for AI Features</li><li>Managing continuous improvement across the Agile process</li><li>Module Summary and Assessment</li></ul><div><strong>Module 4: Final Project and Assessment</strong></div><ul><li>Final Project Submission</li><li>Final Graded Quiz</li><li>Course Wrap-Up</li></ul><div><strong>Module 5: Certifications, Portfolio Development, and Job Readiness</strong></div><ul><li>Product Owner Certifications</li><li>Your Personal Portfolio and Job Search Guidance</li></ul><h1><strong>Course Learning Objective</strong></h1><ul><li>Apply product owner skills learned from all courses in the Product Owner professional certificate</li><li>Analyze a series of Agile scenarios and create key scrum artifacts and deliverables</li><li>Demonstrate the product owner’s role in each of the Agile ceremonies by working through a simulated Scrum project</li><li>Identify the Product Owner’s role in communication and collaboration among all stakeholders throughout the simulated Scrum project</li><li>Create summary documentation for the Sprint Retrospective based on the scenarios provided</li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>Prior knowledge of Product Owner skills, tools, communication, stakeholder management, Agile, Scrum, and IT fundamentals is essential.</div>
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Product Owner Capstone

This course provides an opportunity for you to apply the Product Owner knowledge and skills learned throughout all prior courses in the Product Owner Professional Certificate (PC) program. This course will reinforce your preparation for the Professional Scrum Product Owner (PSPO) or other Product Owner certifications.  We encourage you to complete all courses in this Professional Certificate series before beginning this final course. You will be provided with a real-world Agile project scenario and numerous hands-on activities to further develop your Product Owner skills and experience. 

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  <div><br />Course Introduction <br /><br /></div><div>Elevate your career prospects with this capstone project, designed to help you showcase job-ready business analysis skills in a real-world scenario. <br /><br /></div><div>This hands-on experience allows you to apply business analysis concepts and techniques through a fictitious case study, Enhancing Operational Efficiency in a Multi-Specialty<strong> </strong>Hospital<strong>. </strong>While working on this project, you’ll apply key business analysis<strong> </strong>skills<strong>, including requirements gathering</strong>, <strong>stakeholder analysis</strong>, <strong>project management</strong>, <strong>process modeling</strong>, and <strong>risk assessment</strong>. Additionally, you’ll use tools like <strong>Excel</strong> for <strong>data analysis</strong> and <strong>Cognos</strong> or <strong>Google Looker</strong> for creating impactful <strong>dashboards</strong>. </div><div> </div><div>This project offers an opportunity to design key artifacts integral to a business analysis project, including a <strong>Business Requirements Document (BRD)</strong>, <strong>Requirements Traceability Matrix (RTM)</strong>, <strong>Stakeholder Analysis and Engagement Plan</strong>, <strong>Process Model Diagram</strong>, <strong>Scope Management Plan</strong> and <strong>Risk Mitigation Plan</strong>. <br /><br /></div><div>To conclude the project, you'll design a professional PowerPoint presentation summarizing your findings, sharpening your analytical and presentation skills. <br /><br /></div><div>By completing this project, you’ll refine your critical thinking and problem-solving abilities and business analysis skills that stand on your resume. Be prepared to power up your business analysis career! <br /><br /></div><div> <br /><br /></div><div><br />Course Objectives  <br /><br /></div><ul><li>Gain practical, in-depth experience that builds confidence and readiness for entry-level roles in business analysis </li><li>Apply the concepts and skills acquired to execute a practical business analysis project </li><li>Showcase proficiency in core business analysis skills, including requirements gathering, stakeholder analysis, process modeling, and risk assessment,  </li><li>Demonstrate proficiency in using relevant business analysis tools and technologies, such as Excel and Lucidchart </li></ul><div> </div>
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Capstone Project: Applying Business Analysis Skills

This project offers an opportunity to design key artifacts integral to a business analysis project, including a Business Requirements Document (BRD), Requirements Traceability Matrix (RTM), Stakeholder Analysis and Engagement Plan, Process Model Diagram, Scope Management Plan and Risk Mitigation Plan. To conclude the project, you'll design a professional PowerPoint presentation summarizing your findings, sharpening your analytical and presentation skills. 

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  <div>This Object-Oriented Programming (OOP) course is designed to empower beginner and experienced software developers with the skills needed to build robust and scalable Java applications. It provides an in-depth exploration of OOP principles, including encapsulation, inheritance, polymorphism, and abstraction, enabling you to create modular and maintainable code.<br /><br /></div><div>You will gain hands-on experience designing classes, implementing constructors, and mastering advanced OOP concepts such as interfaces, abstract classes, and method overloading. The course also covers collections, including lists, sets, queues, and maps, ensuring efficient data management in complex applications.<br /><br /></div><div>Additionally, you will learn essential file-handling techniques, work with dates and times, and integrate external services to enhance application functionality. Throughout the course, best practices in software design, such as modularity, code readability, and exception handling, are emphasized, preparing you for real-world software development challenges.</div><div>This course is part of the Java Development from IBM Professional Certificate, a structured learning path designed to provide practical programming experience. It is an ideal starting point for aspiring Java developers.<br /><br /></div><h1>Course Syllabus</h1><div><strong>Module 1: Object-Oriented Programming in Java</strong></div><ul><li>Learn core OOP principles: classes, objects, encapsulation, constructors</li><li>Explore inheritance, polymorphism, interfaces, abstract classes</li><li>Hands-on labs to apply OOP concepts in Java</li></ul><div><strong>Module 2: Advanced Object-Oriented Programming Concepts</strong></div><ul><li>Delve into inheritance, polymorphism, interfaces, inner classes</li><li>Apply concepts through real-world examples and practical labs</li></ul><div><strong>Module 3: The Java Collections Framework</strong></div><ul><li>Study lists, sets, queues, and maps for data management</li><li>Hands-on practice selecting the right collection type for different challenges</li></ul><div><strong>Module 4: File Management</strong></div><ul><li>Learn file handling: creating, reading, writing files, managing directories</li><li>Hands-on with file streams for input/output operations</li></ul><div><strong>Module 5: Handling Dates and Times and Service Integration</strong></div><ul><li>Work with Java’s date and time classes, including time zones and date calculations</li><li>Master time-based functionality for real-world applications</li></ul><div><strong>Module 6: Final Project and Course Summary</strong></div><ul><li>Apply concepts learned in a final project</li><li>Course review and consolidation of Java programming skills</li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>Let your learners know what technology and skills they'll need prior to starting this guided project. Remember that the IBM Skills Network Labs environment comes with many things pre-installed (e.g. Docker) to save them the hassle of setting everything up. Also note that this platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari.<br /><br /></div><h1><strong>Course objectives</strong></h1><div>After completing this course, you will be able to:</div><ul><li>Apply Object-Oriented Programming (OOP) concepts</li><li>Define and implement classes and objects, demonstrating encapsulation and abstraction</li><li>Use advanced OOP concepts such as inheritance, polymorphism, interfaces, and method overloading</li><li>Employ the Java collections framework to manage data using lists, sets, queues, and maps</li><li>Read and write to files using Java streams</li><li>Handle directories effectively</li><li>Work with Java date and time classes for effective date manipulation and formatting</li><li>Integrate external services and call external APIs</li><li>Demonstrate your knowledge with the completion of a comprehensive Java programming project</li></ul><h1><strong>Prerequisites and Hardware Requirements</strong></h1><div>This course is recommended for budding and experienced software developers who want to understand object-oriented programming (OOP) in Java. To access the course materials, you will need a laptop or desktop with internet access.</div>
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Object Oriented Programming in Java

Java developers with object-oriented programming skills in the United States earn an average annual salary of approximately USD 91,024 (Indeed.com).In this course, you’ll gain hands-on experience with key OOP concepts such as encapsulation, abstraction, inheritance, and polymorphism. Learn to define and implement classes and objects. You’ll explore advanced techniques, including interfaces, method overloading, and the Java collections framework, managing data with lists, sets, queues, and maps, and handling file input/output operations.

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  <div>Over 90% of Fortune 500 companies use Java (Netguru) and entry-level Java developers can earn USD100,000+ per year with outstanding opportunities for swift salary growth. This course gives you the job-ready, fundamental skills to develop Java applications employers are looking for, and enables you to add this powerful, versatile language to your resume in just 4-5 weeks!  </div><div>During the course, you’ll learn to set up the Java environment and install and configure the Java Development Kit (JDK), Java Runtime Environment (JRE), and an Integrated Development Environment (IDE). You’ll explore key Java concepts, such as data types, variables, operators, and control flow statements. Plus, you’ll discover how arrays, strings, methods, and effective exception handling are used to create efficient programs.  <br /><br />Throughout, you’ll engage in hands-on exercises and, at the end, you’ll complete a final project covering a real-world scenario that reinforces your practical understanding.  <br /><br />If you’re keen to add Java to your resume and have job-ready experience you can talk about in interviews, enroll today and power up new career opportunities in just one month!<br /><br /></div><h1>Course Syllabus</h1><div><strong>Module 1:</strong> Introduction to the Java Environment and Ecosystem</div><div><strong>Module 2:</strong> Basic Programming Constructs in Java</div><div><strong>Module 3:</strong> Control Structures and String Handling</div><div><strong>Module 4:</strong> Robust Exception Handling</div><div><strong>Module 5:</strong> Final Project and Course Wrap-Up</div><div><br /></div><h1>Learning Objectives</h1><ul><li>Set up the Java environment</li><li>Install and configure the Java Development Kit (JDK), Java Runtime Environment (JRE), and Integrated Development Environment (IDE)</li><li>Describe the Java ecosystem and its applications across web, desktop, embedded, and mobile platforms</li><li>Explain Java fundamental coding concepts</li><li>Identify and utilize various programming constructs, including data types, variables, operators, and control flow statements</li><li>Work with arrays, strings, and methods to create efficient Java programs</li><li>Handle exceptions and manage data structures</li><li>Implement exception-handling techniques using try-catch blocks</li><li>Discuss the differences between checked and runtime exceptions</li><li>Synthesize knowledge gained throughout the course to complete a comprehensive project</li><li>Prepare for technical interviews through practice questions related to Java programming</li></ul><h1>Recommended Skills Prior to Taking this Course</h1><div>This course is recommended for budding and experienced software developers who want to understand Java programming.  If you are new to software development, <a href="https://www.coursera.org/learn/introduction-to-software-engineering?">Introduction to Software Engineering </a>is a recommended prerequisite. You will need a laptop or desktop with internet access to access the course materials.   </div>
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Java Programming for Beginners

This course is designed to equip beginner and experienced software developers and programmers with the essential skills to write and understand Java programs. It comprehensively explores fundamental programming concepts such as variables, data types, and control structures.You will gain hands-on experience writing functions and methods, understanding class structures, and implementing encapsulation, inheritance, and polymorphism. The course also delves into core technical practices and exception handling, enabling you to write efficient and error-free code.

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  <div>The global recommendation engine market is predicted to grow 37% annually through 2030 (Straits Times). The expertise to predict user preferences and drive engagement using AI recommendation system skills has become an essential business need and a highly sought-after skill using vector databases. </div><div>In this IBM mini-course, you’ll create two shareable projects that demonstrate your proficiency and readiness to develop AI-powered recommendation systems.  <br /> <br />You’ll get step-by-step instructions to create a real-life inspired food ordering recommendation system using Chroma DB and Hugging Face models. For your final project, you’ll use Chroma DB or your choice of PostgreSQL, Cassandra, or MongoDB to create a real-life job search recommendation system. This will demonstrate your ability to generate embeddings and implement similarity searches using Hugging Face natural language processing (NLP) algorithms. <br /> <br />Ready to start? Bring your vector, NoSQL, or relational database vector search skills to this course. If you don't already have these skills, you can attain these skills in other Vector Databases Fundamentals Specialization courses.  <br /> <br />Enroll today in this mini-course to advance your AI career!</div><h1>Course Syllabus</h1><ul><li>Welcome</li><li>Introduction to Hugging Face</li><li>Practice project</li><li>Final project</li></ul><h1>Learning Objectives</h1><ul><li>Develop a recommendation system using the ChromaDB vector database and Hugging Face tools </li><li>Generate vector embeddings using natural language processing algorithms</li><li>Perform similarity searches using vector embeddings </li><li>Search a database using a PDF as the input query</li></ul><div><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>This course is the culminating project course in the series linked above. We highly recommend that you take the previous courses before this one.</div><div>To get the most out of this course, you should be comfortable with the following:</div><div>Technologies:</div><ul><li>JavaScript</li><li>ChromaDB</li><li>Git and GitHub</li></ul><div>Concepts:</div><ul><li>Vector embeddings</li><li>Natural language processing concepts</li><li>Recommendation systems</li><li>Similarity searches</li></ul><div>If you take the previous courses in the series, you will learn what you need for success in this course.</div>
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Vector Database Projects: AI Recommendation Systems

In this course, you will apply your knowledge of vector databases, natural language processing, embeddings, and similarity searches to create database-backed recommendation systems in a practice project and a final capstone project using JavaScript and ChromaDB.This course is the last in a series of microcourses. Follow the link below to read about the series and see how this program can benefit you and advance your career.

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  <div>In this course, you will learn how to design, deploy, load, manage, and query data warehouses and data marts. You will begin by understanding different analytics repositories including data marts, data warehouses, data lakes, data lakehouses and their functions and uses. They are designed to enable rapid business decision-making through accurate and flexible reporting and data analysis. <br /> </div><div>A data warehouse is one of the most fundamental business intelligence tools in use today, and one that successful Data Engineers must understand. Additionally, you will learn to design, model, and implement data warehouses and explore data warehousing architectures such as Star and Snowflake schemas. Finally, you will learn how to populate data warehouses using Extract, Transform, and Load (ETL) and Extract, Load, Transform (ELT) processes, verify data, query data, and use Cubes, Rollups, and materialized views/tables.  <br /><br /></div><div>The hands-on labs in this course will enable you to apply what you learn and gain a practical knowledge of data warehousing. You will work with repositories such as PostgreSQL and IBM Db2.</div><div><br /></div><h1>Course Syllabus</h1><ul><li>Module 1: Data Warehouses, Data Marts, and Data Lakes</li><li>Module 2: Designing, Modeling, and Implementing Data Warehouses</li><li>Module 3: Final Assignment and Final Quiz</li><li>Module 4 (Optional): IBM Db2</li></ul><div><br /></div><h1>What you'll learn</h1><ul><li>Discuss the fundamentals of data warehouses, data marts, and data lakes by analyzing their features and advantages.</li><li>Examine organizations' decision-making processes to select specific data warehouse systems and vendors.    </li><li>Design and populate a data warehouse, and model and query data using Cubes, Rollups, and materialized views.</li><li>Design and load data into a data warehouse, write aggregation queries, create materialized query tables, and create an analytics dashboard.</li></ul><h1><strong>Prerequisites</strong></h1><div>To succeed in this course, it is recommended that you should have a fundamental knowledge of Relational Database Management Systems (RDBMS) and a working knowledge of Structured Query Language (SQL).</div><div><br /></div>
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Data Warehouse Fundamentals

This course introduces you to designing, implementing and populating a data warehouse and analyzing its data using SQL & Business Intelligence (BI) tools. This course also covers data repositories including data marts, data lakes, data reservoirs, and Cognos Analytics for visualization. 

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  <div>This course is designed for everyone, including professionals, executives, students, and enthusiasts, interested in learning about generative AI and leveraging its capabilities in their work and lives.   <br /><br /></div><div>This course is your first step toward understanding the capabilities of generative AI, powered by different models, including large language models (LLMs).   <br /><br />In this course, you will learn about the fundamentals and evolution of generative AI. You will explore the capabilities of generative AI in different domains, including text, image, audio, video, virtual worlds, code, and data. You will understand the applications of generative AI across different sectors and industries. You will learn about the capabilities and features of common generative AI models and tools, such as GPT, DALL-E, Stable Diffusion, and Synthesia.    <br /><br />Hands-on labs, included in the course, provide an opportunity to explore the use cases of generative AI through IBM Generative AI Classroom and popular tools like ChatGPT. You will also hear from the practitioners about the capabilities, applications, and tools of generative AI.</div>
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Introduction to Generative AI

Dive into the new and exciting world of Generative AI and Large Language Models (LLM). Understand the capabilities of generative AI and its use cases in the real world. Identify the applications of generative AI in different sectors and industries. Explore common generative AI models and tools for text, code, image, audio, and video generation.

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  <div>This course is designed for everyone, including professionals, executives, students, and enthusiasts interested in leveraging effective prompt engineering techniques to unlock the full potential of generative artificial intelligence (AI) tools like ChatGPT and other LLMs. <br /><br /></div><div>Prompt engineering is a process to effectively guide generative AI models and control their output to produce desired results. In this course, you will learn the techniques, approaches, and best practices for writing effective prompts. <br /><br />You will learn about prompt techniques like zero-shot and few-shot, which can improve the reliability and quality of large language models (LLMs). You will also explore various prompt engineering approaches like Interview Pattern, Chain-of-Thought, and Tree-of-Thought, which aim at generating precise and relevant responses. <br /><br />You will be introduced to commonly used prompt engineering tools like IBM watsonx Prompt Lab, Spellbook, and Dust.  <br /><br />The hands-on labs included in the course offer an opportunity to optimize results by creating effective prompts in the IBM Skills Network AI Classroom. You will also hear from practitioners about the tools and approaches used in prompt engineering and the art of writing effective prompts.<br /><br /></div>
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Introduction to Prompt Engineering

Learn how to improve the effectiveness of your AI by using the latest prompt engineering techniques. Apply best practices for creating prompts and explore examples of impactful prompts. Practice common prompt engineering techniques and approaches for writing effective prompts for different LLMs.

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  <div>Complete the IBM Cybersecurity Analyst Professional Certificate with this final assessment!<br /><br /></div><div>In taking this final course, you will be showing your accumulated knowledge of cybersecurity skills. You will demonstrate your proficiency in cybersecurity tools and concepts including:<br /><br /></div><ul><li>Roles and responsibilities</li><li>System administration</li><li>Network security and vulnerabilities</li><li>Penetration testing and incident response</li><li>Threat intelligence</li></ul><div><br /></div><div>Before completing this final assessment, it is highly recommended that you complete the previous seven courses in the IBM Cybersecurity Analyst Professional Certificate.</div><div><br /><br /></div><ul><li><strong>Prerequisites:</strong><br />It is recommended to take the previous courses in the IBM Cybersecurity Fundamentals Professional Certificate before completing this course.</li></ul><div><strong><br />What you'll learn<br /></strong><br /></div><ul><li>Apply fundamental cybersecurity skills for a final assessment</li><li>Demonstrate proficiency in identifying cyber threats and risks</li><li>Showcase your knowledge of network security and testing for vulnerabilities</li><li>Exhibit an understanding of important concepts and tools utilized by successful cybersecurity analysts</li></ul><div><strong><br />Syllabus<br /></strong><br /></div><div>Module 1: IBM Cybersecurity Fundamentals Professional Certificate Assessment Exam</div>
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IBM Cybersecurity Assessment & CompTIA Security+ Practice Ex

Showcase your expertise in Cybersecurity fundamentals in this final assessment of the IBM Cybersecurity Analyst Professional Certificate!

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  <div>Develop a plausible genAI use case suited to your organization using your industry expertise and gain experience with AI tools and best practices by doing so.<br /><br /></div><h1>Course Syllabus</h1><div><strong>Module 1: Develop an AI Use Case for Your Organization</strong></div><ul><li>Prompt Writing</li><li>Formulate A Business Goal with AI Assistance</li><li>Finalize and Evaluate Your Feasible Use Case</li></ul><div><br /></div><h1>About this Course</h1><div>As an executive or business leader, integrating genAI effectively into your organization will be a top priority. Navigating how to strategically use this tool is both critical and pressing. This short course guides you through formulating effective genAI use cases specific to your business circumstances.<br /><br /></div><div>Through a series of hands-on activities, you will apply genAI best practices while using IBM’s watsonx AI engine to both understand its nature and to brainstorm ways you can leverage this powerful tool in your organization. You will then construct a potential use case yourself. You will:</div><ul><li>Write effective prompts</li><li>Develop a business goal suited to your organization’s pain points</li><li>Choose an appropriate genAI application that addresses your goal</li><li>Critically examine the feasibility of your use case by considering common risks and reflect on ways to mitigate them</li></ul><div>Throughout the course, you will draw on your own industry expertise and leverage that knowledge to refine your ideas for your organization with genAI assistance.<br /><br /></div><div>To get the most from this course, we recommend you take the first 2 courses in the Generative AI for Executives and Business Leaders series as well. You should also be familiar with basic AI terminology and common AI business concepts.</div><div><br /></div>
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GenAI for Execs & Business Leaders: Formulate Your Use Case

Explore generative AI applications to enhance your business strategy. This beginner course empowers executives and leaders with fundamental skills in AI governance and response evaluation. Master prompt engineering for effective AI communication and introduce AI solutions tailored for business needs. Elevate decision-making and drive innovation in your organization through practical, AI-driven insights.

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  <div>Explain to a potential learner why this course is important/interesting and what they can gain from taking your course.<br /><br /></div><h1>Course Syllabus</h1><div><strong>Welcome</strong></div><ul><li>Course Preview</li><li>Course Overview</li><li>Learning Objectives and Syllabus</li><li>Grading Scheme</li></ul><div><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>Final Quiz and Course Wrap Up</strong></div><ul><li>Graded Quiz: Final Quiz</li><li>Reading: Congratulations and Next Steps</li><li>Reading: [Optional] Video Companion Guidebooks</li><li>Reading: Thanks from the Course Team</li></ul><div><strong>Course Rating and Feedback</strong></div><ul><li>Course Rating and Feedback</li></ul><div><strong>Completion Certificates and Badges</strong></div><ul><li>Claim your Badge Here and FAQ</li></ul><div><br /></div><h1>About this Course</h1><div>As an executive or business leader, determining how to integrate AI well is likely a top priority for your organization. In this short course, IBM thought leaders share their perspectives about effectively integrating generative AI at the enterprise level. Listen to their advice and gain insights into these key areas for your own 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, you’ll learn from these leaders' experiences and expertise cross-functionally and within specific domains regarding generative AI potential. Join us in helping you get started with practical guidance on AI adoption! No prior AI background required.</div><div><br /></div>
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GenAI for Execs & Business Leaders: Integration Strategy

Learn how executives and business leaders can strategically integrate Generative AI. Discover practical AI use cases and applications to enhance your enterprise. Understand governance and compliance to ensure responsible AI deployment. Gain beginner-level expertise in leveraging cutting-edge Artificial Intelligence solutions.

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  <div>Much of the world's data lives in databases. SQL (or Structured Query Language) is a powerful programming language that is used for communicating with and manipulating data in databases. A working knowledge of databases and SQL is necessary for anyone who wants to start a career in Data Engineering, Data Analytics or Data Science.<br /><br /></div><div><br />The purpose of this course is to introduce relational database (RDBMS) concepts and help you learn and apply foundational and intermediate knowledge of the SQL language. You will start with performing basic Create, Read, Update and Delete (CRUD) operations using CREATE, SELECT, INSERT, UPDATE and DELETE statements. You will then learn to filter, order, sort, and aggregate data. You will also work with functions, perform sub-selects and nested queries, as well as access multiple tables in the database.<br /><br /></div><div><br />The emphasis in this course is on hands-on, practical learning. As such, you will work with real database systems, use real tools, and real-world datasets. You will create a database instance in the cloud. Through a series of hands-on labs, you will practice building and running SQL queries. At the end of the course you will apply and demonstrate your skills with a final project.<br /><br /></div><div><br />The SQL skills you learn in this course will be applicable to a variety of RDBMSes such as MySQL, PostgreSQL, IBM Db2, Oracle, SQL Server and others. No prior knowledge of databases, SQL or programming is required, however some basic data literacy is beneficial.<br /><br /></div>
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Introduction to SQL

This course introduces the fundamentals of Structured Query Language (SQL) and provides practical experience with real-world databases and tools to perform CRUD operations and manipulate data.

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  <div>In this capstone project, you will apply the skills and knowledge you’ve gained from the IBM Relational Database Administrator Professional Certificate to some real-world database administration tasks. <br /><br />You’ll begin by setting up an Online Transaction Processing (OLTP) database,  creating a data warehouse using PostgreSQL, and querying the databases using SQL.  <br /><br />Next, you’ll build Extract, Transform, and Load (ETL) pipelines with Apache Airflow to automate data flow across systems. Plus, you’ll implement backup and restore processes, starting with manual setups and then progressing to automated solutions.  <br /><br />To ensure database security, you’ll then configure user access and permissions in MySQL. You’ll optimize memory usage and improve query performance to enhance database efficiency.   <br /><br />By the end of the course, you’ll have demonstrated and applied key aspects of database administration. An ideal addition to your portfolio! <br /><br />If you’re looking to get hands-on experience in database administration that you can talk about in interviews, enroll today!</div><div><br /></div><h1>Course Syllabus</h1><div><strong>Module 1: Data Platform Architecture and OLTP Database</strong></div><div>In this module, you will design a data platform that uses MySQL as an OLTP database. You will be using MySQL to store the OLTP data.</div><div><br /><strong>Module 2: Build a Data Warehouse</strong></div><div>In this module, you will design and implement a data warehouse and you will then generate reports from the data in the data warehouse.</div><div><br /><strong>Module 3: ETL and Data Pipelines<br /></strong>In this module, you will use the given Python script to perform various ETL operations that load the data warehouse with the required information. You will also write a pipeline that analyzes the web server log file, extracts the required lines and fields, and transforms and loads data.</div><div><br /></div><div><strong>Module 4: Backup, Restore, and Automation</strong></div><div>In this module, you will perform back-up and restore operations on the database. You will also implement automation to streamline database management processes.</div><div><br /><strong>Module 5: Database Optimization, Security, and Access Management</strong></div><div>In this module, you will optimize the query performance and memory utilization of your database. You will also implement data security protocols, including user access management strategies and data encryption.</div><div><br /><strong>Module 6: Final Submission</strong></div><div>In this module, you will organize and submit your project deliverables for final evaluation. This module guides you through the submission process and outlines the criteria for assessing your work. </div><div><br /></div><h1>Recommended Skills Prior to Taking this Course</h1><div>Proficiency in SQL, MySQL, PostgreSQL, ETL, Apache Airflow, and database creation, administration, and security principles is required.</div>
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Relational Database Administration Capstone Project

In this capstone project, you will apply the skills and knowledge you’ve gained from the IBM Relational Database Administrator Professional Certificate to some real-world database administration tasks. 

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  <h1>Exploring Partial Dependence Plots with Python: A Guided Journey</h1><div><br />Understanding how <strong>machine learning models</strong> make predictions is essential, especially in fields where data-driven decisions can significantly impact outcomes. In this hands-on guided project, you will explore the power of <strong>Partial Dependence Plots (PDPs)</strong> using <strong>Python</strong> and <strong>scikit-learn</strong>, focusing on two real-world datasets: the Titanic dataset for survival prediction and the Melbourne Housing dataset for price estimation.<br /><br /></div><div><br />Throughout this project, you will not only build predictive models but also learn to interpret their outputs, revealing how features such as age, fare, rooms, distance, and land size influence predictions. By visualizing these relationships through PDPs, you will gain insights into model behavior, enabling you to communicate findings effectively to stakeholders.<br /><br /></div><div><br />In just 30 minutes, you will develop a practical understanding of PDPs and their role in explaining machine learning models, equipping you with the skills to navigate the intersection of AI and real-world applications.<br /><br /></div><div>We also provide an Advanced Project of <strong>Partial Dependence Plot.</strong><br />You can take it here: <a href="https://cognitiveclass.ai/courses/med-prediction-with-explainable-ai-partial-dependence-plot">https://cognitiveclass.ai/courses/med-prediction-with-explainable-ai-partial-dependence-plot</a><br /><br /><strong><br />What You'll Learn<br /></strong><br /></div><div><br />By the end of this project, you will have mastered:<br /><br /></div><ul><li>Interpreting machine learning models using Partial Dependence Plots (PDPs) to visualize feature impacts.</li><li>Analyzing the influence of key features in both classification (Titanic dataset) and regression (Melbourne Housing dataset) contexts.</li><li>Applying Gradient Boosting Classifier and Regressor models to real-world datasets.</li></ul><div><strong><br />What You'll Need<br /></strong><br /></div><div><br />To get started with this guided project, you should have:<br /><br /></div><ul><li>A basic understanding of Python programming.</li><li>Access to modern web browsers like Chrome, Edge, Firefox, Internet Explorer, or Safari.</li></ul><div><br />Ready to unlock the insights hidden within your data? Start this guided project now and empower yourself to interpret complex algorithms, transforming raw data into actionable insights for decision-making in various domains.<br /><br /></div><div><br /></div>
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Partial Dependence Plot Applied to House Pricing Models

Partial Dependence Plots (PDPs), is a common techniques to interpret machine learning models by visualizing feature impacts on predictions. This lab explores the relationships between features such as rooms, distance, and landsize in the Melbourne Housing dataset, as well as age and fare in the Titanic dataset, translating complex model predictions into clear insights. Data scientists and stakeholders can leverage PDPs to gain a deeper understanding of model behavior, enhancing both technical analysis and informed business decision-making.

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  <div>Please Note: Learners who successfully complete this IBM course can earn a skill badge —a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!<br /><br /></div><div><br />Kickstart your learning of Python for data science, as well as programming in general with this introduction to Python course. This beginner-friendly Python course will quickly take you from zero to programming in Python in a matter of hours and give you a taste of how to start working with data in Python.<br /><br /></div><div><br />Upon its completion, you'll be able to write your own Python scripts and perform basic hands-on data analysis using our Jupyter-based lab environment. If you want to learn Python from scratch, this course is for you.<br /><br /></div><div><br />You can start creating your own data science projects and collaborating with other data scientists using IBM Watson Studio. When you sign up, you will receive free access to Watson Studio. Start now and take advantage of this platform and learn the basics of programming, machine learning, and data visualization with this introductory course.<br /><br /></div>
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Python Basics for Data Science

Start your journey in data science and programming with this beginner-friendly Python course. Earn a verifiable digital credential and free access to IBM Watson Studio upon completion.

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  <div>Generative AI for Data Analytics demonstrates the integration of Generative Artificial Intelligence (Generative AI) in Data Analytics. This course demonstrates how to use generative AI tools/frameworks/platforms to increase the effectiveness of various processes involved in the data analytics life cycle.  </div><div>You will learn how to use and apply generative models for tasks such as data generation, data augmentation, data preparation and finding insights, querying databases, generating visualizations, and storytelling. You will also explore challenges and ethical considerations associated with using Generative AI in data analytics.</div><div><br /></div><h1><strong>Course Syllabus</strong></h1><div><strong>Module 1: Data Analytics and Generative AI   </strong></div><ul><li><ul><li>Module Introduction and Learning Objectives </li><li>Video: Generative AI for Data Analytics  </li><li>Reading: Generative AI Tools for Data Analytics  </li><li>Video: Examples of Generative AI in Data Analytics  </li><li>Reading: Case Study on Successful Implementations of Generative AI in Data Analytics  </li><li>Lab: Explore a Simple Generative Tool </li><li>Discussion Prompt: Discuss How Generative AI Can Be Used in Data Analytics</li><li>Reading: Leveraging Generative AI in Data Analytics Process  </li><li>Demo Video: Generative AI for Data Generation and Augmentation  </li><li>Lab: Generative AI for Data Generation and Augmentation  </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><li>Video: Generative AI for Q and A Model Insights  </li><li>Lab: Generative AI for Q and A Model Insights  </li><li>Video: Successful Implementations of Generative AI for Data Preparation and Insights   </li><li>Reading: Using Microsoft Copilot   </li><li>Module 1 Practice Quiz: Data Analytics and Generative AI </li><li>Module 1 Summary: Data Analytics and Generative AI   </li><li>Module 1 Cheat Sheet: Data Analytics and Generative AI </li><li>Module 1 Graded Quiz: Data Analytics and Generative AI  </li></ul></li></ul><div><strong>Module 2: Use of Generative AI for Data Analytics  </strong> </div><ul><li><ul><li>Module Introduction and Learning Objectives </li><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 for Creating Dashboards  </li><li>Lab: Generative AI for Creating Dashboards </li><li>Video: Generative AI for Storytelling  </li><li>Lab: Generative AI for Storytelling  </li><li>Discussion Prompt: Dashboarding and Storytelling with Generative AI  </li><li>Video: Successful Implementations of Generative AI for Data Visualization and Storytelling  </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><li>Module 2 Practice Quiz: Use of Generative AI for Data Analytics </li><li>Module 2 Summary: Generative AI for Data Visualization and Storytelling  </li><li>Module 2 Cheat Sheet: Use of Generative AI for Data Analytics </li><li>Module 2 Graded Quiz: Use of Generative AI for Data Analytics  </li></ul></li></ul><div><strong>Final Project and Exam    </strong></div><ul><li><ul><li>Reading: Guided Practice Project Overview  </li><li>Lab: Guided Practice Project </li><li>Final Exam  </li></ul></li></ul><div><strong>Course prerequisites:  </strong></div><div>An introductory knowledge of data analytics, generative AI, and prompt engineering would be beneficial.  You can check these courses to get a basic understanding of these topics:</div><ul><li><a href="https://www.coursera.org/learn/generative-ai-introduction-and-applications?specialization=applied-artifical-intelligence-ibm-watson-ai">Generative AI: Introduction and Applications</a></li><li><a href="https://www.coursera.org/learn/generative-ai-prompt-engineering-for-everyone?specialization=applied-artifical-intelligence-ibm-watson-ai">Generative AI: Prompt Engineering Basics</a></li></ul><h1>Learning Objectives</h1><ul><li>Describe how you can use Generative AI tools and techniques in the context of data analytics across industries</li><li>Implement various data analytic processes such as data preparation, analysis, visualization, and storytelling using Generative AI tools</li><li>Evaluate real-world case studies showcasing the successful application of Generative AI in deriving meaningful insights </li><li>Analyze the ethical considerations and challenges associated with using Generative AI in data analytics</li></ul>
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Mastering Generative AI for Data Analytics

Enhance your data analytics career by mastering generative AI for data analysis. Learn intermediate-level skills in artificial intelligence and data augmenting techniques. Explore querying databases to improve data insights. Unlock new potential in your analytics projects. Unlock Generative AI data analytics skills: prompting, visualization, storytelling, and querying. Explore concepts, industry uses, and ethics. Designed for analysts with Python & AI basics, complete a shareable project and earn an industry certificate.

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  <ul><li>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.</li></ul><div><br /></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><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><h1><strong>Course Syllabus:</strong></h1><div><strong>Introduction to Deep Learning and Neural Networks</strong>   </div><div><br /></div><ul><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> <strong>Basics of Deep Learning </strong> </div><div><br /></div><ul><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><strong>Keras and Deep Learning Libraries  </strong> </div><div><br /></div><ul><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><strong>Deep Learning Models   </strong></div><div><br /></div><ul><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><strong>Final Project </strong> </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><strong>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>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><ul><li>Working knowledge of Python.</li><li>Machine Learning with Python</li></ul>
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Deep Learning Fundamentals 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><strong>Course Description:</strong><br /><br />In today’s data-driven world, the ability to design and manage effective Enterprise Data Architecture (EDA) is a highly sought-after skill for career opportunities in data strategy, architecture, and management. Whether you want to advance in your current role as a data engineer or database administrator or transition to a specialized position like a data architect or enterprise data strategist, this course provides the essential knowledge and skills to succeed in the in-demand field of EDA.<br /><br />Gain foundational knowledge of EDA, exploring its core components, including data models and industry-standard frameworks such as TOGAF and the Zachman Framework. This will enable you to design architecture that meets specific business requirements. <br /><br />Practical insights into ETL processes, DataOps practices, and lifecycle management provide a strong foundation for managing data operations. <br /><br />Through hands-on labs and projects, you will gain skills in designing, optimizing, and managing enterprise data architectures, ensuring efficient storage, processing, migration, and governance.<br /><br />With a mix of theory, activities, and labs, you’ll acquire the expertise needed to excel in enterprise data architecture.  <br /><br /><strong>Target Audience:</strong><br /><br />This course is designed for aspiring data architects who want to pursue a career in data architecture and design. <br />It is also beneficial for data engineers who want to expand their skills in data architecture and operations.<br /><br />This course is meant for professionals with a background in data engineering, database administration, system architecture, or data analysis.  <br /><br /><br /></div>
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Enterprise Data Architecture and Operations

This course is designed for aspiring data architects, engineers, IT professionals, and database administrators who want to optimize their organization’s data systems and operations. 

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  <div>Are you ready to dive into the world of data engineering? You’ll need a solid understanding of how data is stored, processed, and accessed. You’ll need to identify the different types of database that are appropriate for the kind of data you are working with and what processing the data requires.<br /><br /></div><div><br />In this course, you will learn the essential concepts behind relational databases and Relational Database Management Systems (RDBMS). You’ll study relational data models and discover how they are created and what benefits they bring, and how you can apply them to your own data. You’ll be introduced to several industry standard relational databases, including IBM DB2, MySQL, and PostgreSQL.<br /><br /></div><div><br />This course incorporates hands-on, practical exercises to help you demonstrate your learning. You will work with real databases and explore real-world datasets. You will create database instances and populate them with tables.<br /><br /></div><div><br />No prior knowledge of databases or programming is required.<br /><br /></div><div><br />Anyone can audit this course at no-charge. If you choose to take this course and earn the Coursera course certificate, you can also earn an IBM digital badge upon successful completion of the course.<br /><br /></div>
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Introduction to Relational Databases (RDBMS)

Learn essential concepts of relational databases and RDBMS with this course. Hands-on exercises with real databases and datasets, no prior knowledge required. Audit for free, earn a certificate and IBM digital badge.

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  <div>This is the final course in the IBM Backend Development Professional Certificate. This capstone project course will give you the chance to practice the work that back-end developers do in real life when working with applications. <br /><br />You will be tasked to develop, deploy, and integrate an application composed of several microservices, and integrating them seamlessly. You will use various back-end tools and technologies, including Flask, Django, NoSQL, and Mongo DB, to accomplish this project while applying coding best practices. You will also deploy and scale and update each of the application’s microservices independently using Cloud Native technologies: Kubernetes, OpenShift and serverless (Code Engine).<br /><br />In this course, you’ll mostly focus on hands-on work to demonstrate and apply what you have learnt in previous courses. By successfully completing this Capstone you will have added a project to your programming portfolio to showcase to employers.<br /><br /></div>
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Back-end Application Development Capstone Project

In this course, you will assume the role of a real-life developer working to develop and deploy back-end microservices and applications. 

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  <div>This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes.<br /><br /></div><div><br />By the end of this course you should be able to:<br /><br /></div><ul><li>Differentiate uses and applications of classification and classification ensembles</li><li>Describe and use logistic regression models</li><li>Describe and use decision tree and tree-ensemble models</li><li>Describe and use other ensemble methods for classification</li><li>Use a variety of error metrics to compare and select the classification model that best suits your data</li><li>Use oversampling and undersampling as techniques to handle unbalanced classes in a data set</li></ul><div><br />Who should take this course?<br /><br /></div><div><br />This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Classification techniques in a business setting.<br /><br /></div><div><br />What skills should you have?<br /><br /></div><div><br />To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.<br /><br /></div>
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Supervised Machine Learning: Classification

This course teaches the basics of Classification in Supervised Machine Learning, with a focus on using best practices for data sets with unbalanced classes. Ideal for aspiring data scientists. Prerequisites: Python, Data Cleaning, EDA, Calculus, Linear Algebra,

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  <div>This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques.<br /><br /></div><div><br />By the end of this course you should be able to:<br /><br /></div><ul><li>Differentiate uses and applications of classification and regression in the context of supervised machine learning</li><li>Describe and use linear regression models</li><li>Use a variety of error metrics to compare and select a linear regression model that best suits your data</li><li>Articulate why regularization may help prevent overfitting</li><li>Use regularization regressions: Ridge, LASSO, and Elastic net</li></ul><div><strong><br />Who should take this course?</strong><br /> This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Regression techniques in a business setting.<br /><br /></div><div><strong><br />What skills should you have?</strong><br /> To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.<br /><br /></div>
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Supervised Machine Learning: Regression

Learn to build and compare regression models for supervised machine learning. Gain hands-on experience with Python and explore best practices for data analysis. Prerequisites: Python, Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and

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  <p>This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning.</p>

<p>By the end of this course you should be able to:</p>

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  <li>Explain the kinds of problems suitable for Unsupervised Learning approaches</li>
  <li>Explain the curse of dimensionality, and how it makes clustering difficult with many features</li>
  <li>Describe and use common clustering and dimensionality-reduction algorithms</li>
  <li>Try clustering points where appropriate, compare the performance of per-cluster models</li>
  <li>Understand metrics relevant for characterizing clusters</li>
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<p>Who should take this course?<br />
This course targets aspiring data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques in a business setting.</p>

<p>What skills should you have?<br />
To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.</p>
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Unsupervised Machine Learning

This course teaches Unsupervised Machine Learning techniques, from data analysis to clustering algorithms, to help you find insights from unlabeled data. Suitable for aspiring data scientists with Python programming and mathematical fundamentals.

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  <div>This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning. Once you have developed a few  Deep Learning models, the course will focus on Reinforcement Learning, a type of Machine Learning that has caught up more attention recently. Although currently Reinforcement Learning has only a few practical applications, it is a promising area of research in AI that might become relevant in the near future.<br /><br /></div><div>After this course, if you have followed the courses of the IBM Specialization in order, you will have considerable practice and a solid understanding in the main types of Machine Learning which are: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning.<br /><br />By the end of this course you should be able to:<br />Explain the kinds of problems suitable for Unsupervised Learning approaches<br />Explain the curse of dimensionality, and how it makes clustering difficult with many features<br />Describe and use common clustering and dimensionality-reduction algorithms<br />Try clustering points where appropriate, compare the performance of per-cluster models<br />Understand metrics relevant for characterizing clusters<br /><br />Who should take this course?<br />This course targets aspiring data scientists interested in acquiring hands-on experience with Deep Learning and Reinforcement Learning.<br /> <br />What skills should you have?<br />To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Unsupervised Learning, Supervised Learning, Calculus, Linear Algebra, Probability, and Statistics.<br /><br /></div>
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Deep Learning and Reinforcement Learning

This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning.

<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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