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IBM: Introduction to Generative AI

Beginner 6 hours English Course CertificateFREE
SkillsGenerative AIAI conceptsAI toolsGPTImage generationAI literacy

Is this course right for you?

Our take
An IBM edX course that starts by drawing the line between generative and discriminative AI, then surveys what generative AI can actually do across text, image, audio, video, code and data.

Good for: A broad, no-code orientation to generative AI and its tools.

Skip if: You want to build with generative AI or go deep technically.

It grounds the survey in specific tools — GPT, DALL-E, Stable Diffusion — so the capabilities stay concrete rather than abstract, and it does all this without any coding. That makes it a clear, broad orientation for someone who wants to understand the landscape and talk about it credibly, rather than build in it.

If you do want to build, or to go deep technically, treat this as a starting point and move on to a hands-on course afterwards. Audit it free on edX, with a verified certificate as a paid option and financial assistance available; the certificate is a credible learning record rather than a formal qualification. Generative AI moves fast, so some tools may have changed since filming (as of 2026).

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About this course

This course distinguishes generative from discriminative AI, then surveys generative AI's capabilities across text, image, audio, video, code, and data, walking through specific tools (GPT, DALL-E, Stable Diffusion, Synthesia) with hands-on labs through IBM's Generative AI Classroom and tools like ChatGPT, plus video segments from IBM practitioners on real applications across different sectors.

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Instructor

RA
Rav Ahuja
edX instructor

Rav Ahuja is Global Program Director at IBM Skills Network, leading IBM's AI and data science course development on edX and Coursera.

Frequently asked questions

No. It is an introductory, largely non-technical course explaining what generative AI is, how it works at a conceptual level, its capabilities and limitations, and common use cases. There is no coding required, so anyone comfortable using a computer can follow it. The goal is understanding and vocabulary rather than building AI, making it accessible to non-technical professionals and beginners alike.

It focuses on concepts and use cases rather than deep training in specific tools, but it introduces the kinds of generative-AI applications and models in common use — text, image, and code generation — and how they apply in real settings, often with reference to IBM's own AI offerings. Expect a conceptual tour of what these tools do rather than hands-on tutorials in any one product.

Anyone wanting a credible, plain-language understanding of generative AI — business professionals, managers, students, or newcomers exploring the field. It assumes no AI or technical background, so it suits people who need to understand and discuss generative AI at work rather than build it. Developers wanting to actually create AI applications would move on to hands-on, code-based courses afterward.

Reasonably, but generative AI moves extremely fast, so check the recent-update date. The core concepts it teaches — what generative models are, how they are used, their limits — stay valid, but specific tools and capabilities evolve within months. Treat it as a solid conceptual foundation and keep up with current developments separately, since no introductory course can track every new model or feature.

Yes, you can audit it (it appears on Coursera and IBM's own learning platform) to access the material at no cost, with a certificate available for a fee or subscription. Since it is a short, conceptual course, auditing captures most of the value if you simply want to understand generative AI rather than earn the credential.
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