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DeepLearning.AI · on Coursera

Generative AI with Large Language Models

Intermediate English Course CertificateFREE
SkillsLarge language modelsGenerative AIFine-tuningPrompt engineeringTransformersModel deployment

Is this course right for you?

Our take
This is a rare technical introduction to large language models that actually gets under the hood, built by DeepLearning.AI with AWS.

Good for: Developers with ML basics who want to understand and work with large language models.

Skip if: You are new to machine learning, or you only want to use LLMs through a chat interface.

Most 'intro to AI' material stops at the chat window. This one follows the whole arc, from how a model is trained to how fine-tuning reshapes it and how you evaluate and deploy the result, with hands-on labs at each step, so you leave able to build with LLMs, not just prompt them.

That depth is also the barrier to entry: it assumes you can write Python and have met machine learning before. If you mainly want to use tools like ChatGPT well, a non-technical primer is the better place to begin.

You can audit it free on Coursera; only the certificate costs, and financial aid is available. The tooling around LLMs changes month to month, but the fundamentals here, how these models learn and adapt, have not (as of 2026).

Comparison · LBS

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About this course

Generative AI with Large Language Models is the course to take once you've got the concepts and want to get your hands dirty. Built by DeepLearning.AI with AWS, it traces the full lifecycle of an LLM project: how transformers work, how to frame a use case, and the real techniques teams use to make models useful — prompting, fine-tuning, and reinforcement learning from human feedback (RLHF).

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Instructor

AB
Antje Barth
Coursera instructor

Led by Antje Barth, a Principal Developer Advocate for generative AI at AWS and co-author of 'Data Science on AWS,' alongside fellow AWS specialists. The teaching team brings production experience rather than pure theory, which shows in the labs.

Frequently asked questions

It is intermediate, not beginner. Built by DeepLearning.AI with AWS, it assumes comfortable Python and a working understanding of machine learning — ideally some familiarity with deep learning and how models train. It is not an introduction to coding or ML. Arriving with Python and ML grounding is the right preparation; without it, the hands-on labs on training and tuning large language models will be a struggle.

No. Unlike the many conceptual 'AI for everyone' courses, this is a technical, hands-on course about how large language models actually work and how to build with them — including code labs. Non-technical people wanting an overview should take a conceptual course instead. This is aimed at developers and data people who want to understand and work with LLMs at a technical level.

Yes, substantially. It includes practical labs (run in the cloud) where you work with large language models — exploring how they behave, fine-tuning, and applying techniques like reinforcement learning from human feedback. That hands-on element is a core strength, since you learn by actually doing rather than only watching, but it is also why the Python and ML prerequisites genuinely matter.

For a technically-inclined person who wants to genuinely understand and build with large language models, yes — it is well-regarded, covers the LLM lifecycle from training to deployment, and comes from DeepLearning.AI with AWS. The field moves fast, so expect to keep learning after it, but as a solid, hands-on grounding in how LLMs work under the hood it is a strong choice.

You can audit it on Coursera to watch the lectures at no cost, but the hands-on labs — where much of the value is — and the certificate require a subscription. Since the labs are central to actually learning to work with LLMs, the paid track is worth considering here more than for purely conceptual courses. Financial aid is available if cost is a barrier.
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