LLM/ML Courses Roundup: 14 Picks, 3 Tracks (2026)

If you want to actually understand large language models rather than just call an API, this LLM/ML courses roundup is where to start. Nineteen courses came across the desk, fourteen made the cut, and they fall into three honest tracks: foundations, retrieval, and agent engineering. Every pick links to a free, public course from a primary source, with a one-line take on what you get and when to use it.

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The library is organized by what you are trying to build, not by vendor. Start at the bottom of the stack (how a transformer actually works) and climb up (retrieval, then autonomous agents). Three tracks, one path.

Foundations: build from scratch first

Two Karpathy courses anchor the foundations track. Neural Networks: Zero to Hero is eight lectures that start at backprop internals and end with you training a GPT by hand. Language Modeling from Scratch (the nanoGPT lecture) is the capstone. The point: you cannot debug what you cannot see, and these courses make the internals visible. See the full foundations roundup for the two entries plus our inference foundations cross-link.

RAG and vector databases: ground the model in your data

Retrieval-augmented generation is where most production LLM work actually lives. This track covers the full stack: vector storage (Pinecone), embeddings-to-app pipelines, building and evaluating RAG systems, and the accuracy work that separates a demo from a product. Five courses, each covering a distinct layer. See the RAG and vector databases roundup.

Agent engineering: memory, tools, and multi-agent systems

Agents are the 2026 frontier and the most overhyped layer. This track cuts the noise: agent memory, browser and computer-use agents, agent evaluation, multi-agent orchestration, and LLMOps (the production layer most tutorials skip). Six courses plus an LLMOps section. See the agent engineering roundup.

Already covered: cross-link only

Five courses from the original list already have dedicated tutorial posts in our AI Agents series: HuggingFace Agents, MCP with Anthropic, Agent Design Patterns, Berkeley Agent MOOC, and Berkeley Advanced Agents. Read those for depth; this hub points you to them rather than repeating them.

New here and unsure where to start? Do Karpathy first (foundations), then one RAG course (Building and Evaluating RAG apps is the most complete), then one agent course (Agent Memory is the clearest). Three courses, roughly forty hours, and you will understand the full stack well enough to debug production issues.