AI, Deep Learning & RAG — Zero to Hero

What you'll build

This is the deepest course in the AI/ML path: it starts from the actual mechanics of a neural network, builds up to how transformer-based LLMs work, then spends the second half entirely on retrieval-augmented generation — embeddings, vector search, chunking, retrieval, generation, evaluation, and the agentic patterns built on top of it.

By the end, you won't just know how to call an LLM API — you'll understand why RAG systems fail in practice (bad chunking, weak retrieval, silent hallucination) and how to fix each failure mode.

Who this is for

Anyone who wants a genuinely "zero to hero" path into modern AI engineering: no prior deep learning background required, but comfort with Python (see Python for AI & Data) will help once the course gets hands-on.

A note on how this course is built

Every video lesson links to a real, well-known technical talk or tutorial from 3Blue1Brown, Andrej Karpathy, or freeCodeCamp/LangChain — some of the most respected explainers of this material publicly available. The written lessons between them exist to connect those videos into one coherent, ordered curriculum and add the engineering details (production tradeoffs, evaluation, failure modes) the videos don't cover.