Concept Guides
AI & Machine Learning for Developers
Building with AI rather than just using it: embeddings, retrieval, agents and the APIs that tie them together.
Python for AI and Machine Learning
The data-handling habits a working AI/ML developer needs, beyond core Python syntax.
Machine Learning Fundamentals
What it actually means for software to learn from data, in plain English.
Embeddings, Explained
How text, images, or other data get turned into numbers that capture meaning, not just characters.
Vector Databases
How systems store and search millions of embeddings fast enough to be usable.
Retrieval-Augmented Generation (RAG)
Giving a language model relevant documents to read before it answers, instead of relying only on what it memorized.
Working with LLM APIs
Calling a hosted language model from code: prompts, tokens, streaming, and rate limits.
Building AI Agents
Tool calling and the agent loop, from a developer's implementation perspective.