LLM courses in 2026: the 6 best to learn large language models

Generative AI with Large Language Models is the course most people mean when they ask which LLM course to take. DeepLearning.AI built it with AWS, and it is taught by AWS practitioners. Week 1 covers generative AI use cases, the project lifecycle and model pre-training, including scaling laws (how model size, data and compute trade off). Week 2 is about fine-tuning and evaluating large language models. Week 3 covers reinforcement learning from human feedback and LLM-powered applications, with labs on AWS.

It is rated intermediate: DeepLearning.AI says it suits anyone with a background in Python. Coursera estimates 2 weeks at 10 hours a week, and the course page shows 3,661 reviews. You pay through a Coursera subscription at $49 per month, which makes it cheap if you finish in a month. The first module can be previewed for free. You receive a shareable certificate once you pay and complete the programming assignments, within a 180-day eligibility window after which the course must be bought again, and Coursera offers financial aid to learners who cannot afford the fee. Companies can assign it through Coursera for Teams, at $399 per user per year on annual billing ($1,995 a year for 5 seats), with a 14-day refund policy: for one learner who finishes in a month, the $49 individual month is far cheaper, while the team plan pays off for people who will take several courses in the year.

XCS224N is Stanford's professional version of its natural language processing course. It explores the fundamental concepts of NLP and its role in research on large language models, including transformers and pretraining. Coding assignments are in Python, some require basic Linux command line skills, and before your first course in Stanford's AI Professional Program you complete a short application (about 15 minutes) showing proficiency in Python plus college calculus and linear algebra.

It costs $2,045 and asks for 10 to 15 hours per week. You earn a Certificate of Achievement and 10 CEU-equivalent units (continuing education units, which some employers count toward training goals). Course materials stay available for 90 days after the course ends. Groups of five or more can enroll together, and Stanford advises organizations on the best options for their training goals.

Most LLM courses teach you to build. This one teaches you to know whether what you built works. AI Evals For Engineers & PMs, run by Parlance Labs on Maven, is a live cohort course on evaluating LLM applications: error analysis, building tests, and red-teaming before your users find the failures. It is aimed at both engineers and product managers, so a team can take it together.

The format is 33 live sessions and 11 lessons, all recorded, with more than 10 office hours and a community where you can ask questions. One purchase includes lifetime access to the recordings, the materials and future cohorts, so a teammate who misses a session can catch the next run. Plan 3 to 5 hours per week. It costs $4,500 per person. Teams save 20% or more when 2 or more teammates join the same cohort, and Maven offers dedicated cohorts with a custom schedule for a single company. The course provides everything an L&D department asks for: an email template, receipts and a certificate of completion.

Udacity's Applied Generative AI Engineering Nanodegree is the most project-driven option in this ranking. It runs 56 hours across three courses and 80 lessons. The first covers generative AI fundamentals. The second, Large Language Models and Retrieval Augmented Generation, covers prompt engineering, model selection and cost, RAG with vector databases, and evaluation with RAGAS, ending with an end-to-end RAG application. The third moves to multimodal apps with structured outputs and observability. Along the way you adapt models with PEFT (parameter-efficient fine-tuning).

Three projects get expert feedback, which is what separates it from video catalogs. The program is intermediate: Udacity lists intermediate Python, deep learning, database fundamentals and Hugging Face among the knowledge to have before enrolling. The monthly subscription costs $249 per month and includes access to the rest of the catalog; the page also shows a bundle and a one-time individual course option. It is rated 4.9 by 224 learners.

DataCamp's Developing Large Language Models track strings together seven courses: Introduction to LLMs in Python, Working with Llama 3, Natural Language Processing in Python, Transformer Models with PyTorch, Scalable AI Models with PyTorch Lightning, Reinforcement Learning from Human Feedback, and LLMOps Concepts. The whole track takes about 19 hours, with exercises in the browser, so nobody needs to set up a GPU.

DataCamp says there are no prerequisites for the track, though the courses use Python throughout. You earn a Statement of Accomplishment you can add to LinkedIn. Pricing is a subscription: Premium from $11 per month billed annually, and Teams from $11 per user per month billed annually, both shown as a special price on the pricing page. A yearly subscription cannot be paused, unlike a monthly one, so pick monthly if your team will study in bursts.

The Hugging Face LLM Course is the reference free course, and it ranks at the top of Google for good reason. Chapters 1 to 4 introduce the Transformers library and how transformer models work. Chapters 5 to 8 cover Datasets and Tokenizers, classic NLP tasks and LLM techniques. Chapter 9 shows how to build and share demos. Chapters 10 to 12 go into advanced topics: fine-tuning, curating high-quality datasets and building reasoning models.

It is completely free and without ads. Each chapter is designed for one week at about 6 to 8 hours, but you go at your own pace. It asks for a good knowledge of Python, does not expect prior PyTorch or TensorFlow, and works best after an introductory deep learning course. Hugging Face currently offers no certification for this course, so it will not satisfy an employer who wants proof of completion.