Maxime Labonne · on GitHub
A widely starred roadmap in three tracks: optional fundamentals, the LLM Scientist (architecture, pre-training, fine-tuning, preference alignment, evaluation, quantization) and the LLM Engineer (running models, vector stores, RAG, agents, inference optimization, deployment, security). It suits self-directed learners who want a curated map of the best free resources plus runnable notebooks rather than one instructor's course.
Developers and technically curious learners. This is an intermediate course in Generative AI & LLMs, running about 3 parts with curated readings and notebooks, self-paced.
Free. The author states the course will always stay free; a companion book (LLM Engineer's Handbook) is sold separately. Price seen 2026-11-01.
A large catalogue of runnable notebooks, each isolating one retrieval-augmented generation technique, from basic chunking and query transformations to reranking, hierarchical and graph-based retrieval, and evaluation. It suits developers who have built a basic RAG app and want to systematically try the techniques that improve retrieval quality.
Covers the full LLM lifecycle from transformer architecture and pre-training through instruction fine-tuning, RLHF, and deployment, taught by DeepLearning.AI and AWS engineers. The right course for Python developers with basic ML who want to understand how LLMs are actually built and adapted.
Hugging Face's hands-on course on LLMs and NLP using the Transformers, Datasets, Tokenizers, and Accelerate libraries, from fundamentals through fine-tuning, demos, and reasoning models. Best for Python developers who want to work with open models directly rather than only via APIs.