Where should you start?

Each track assumes the ones before it, but you do not have to start at the beginning. Pick the description that fits you best. If a lesson feels too fast, the "Builds on" line under its title links to the earlier lessons it assumes.

• AI is new to meNo math or code needed. Learn what AI is and teach a computer by example.
• I know some math or programmingStart with how models learn: loss, gradients, and why models overfit.
• I know ML basics and want to understand LLMsGo straight to tokens and embeddings, then into transformers.

Prefer to look around first? Browse the full route or open any of the labs.