Paths · Builder
Learn to build with AI
A builder path through LLMs, RAG, agents, APIs, and small shipping projects — interactive labs included.
Core spine
- 01What AI can and can't do9m
- 02Why AI makes mistakes10m
- 03Privacy and smart sharing9m
- 04Your AI learning map8m
- 05What is an LLM?7m
- 06Tokens — how AI reads text6m
- 07Serving Large Language Models9m
- 08Temperature — safe vs creative6m
- 09Embeddings — meaning as numbers7m
- 10Vectors & similarity search7m
- 11RAG — look it up, then answer8m
- 12Inside RAG — the pipeline8m
- 13Tools — when AI takes action7m
- 14Agents — think, act, repeat8m
- 15Multi-Agent Systems9m
- 16MCP — a standard plug for AI tools8m
- 17Good prompts have a job9m
- 18Add context9m
- 19Iterate: bad to better9m
- 20Chain-of-Thought Prompting10m
- 21AI for Research10m
- 22AI for Marketing10m
- 23Spot wrong answers9m
- 24Python only what you need8m
- 25Python dictionaries9m
- 26Classes and objects10m
- 27Python virtual environments9m
- 28Data: tables and simple plots9m
- 29File handling9m
- 30Prediction: your first ML idea9m
- 31Clustering10m
- 32Decision trees10m
- 33Random forests10m
- 34Train a tiny model10m
- 35Loss functions10m
- 36Neural nets by building10m
- 37Deep learning10m
- 38When computers see10m
- 39Git basics10m
- 40GitHub basics10m
- 41Talk to an LLM from code10m
- 42Build a mini RAG11m
- 43Tools in code11m
- 44Model deployment11m
- 45AI Monitoring9m
- 46Production AI Architecture10m
- 47Capstone: ship a tiny AI app12m