Math for AI
Linear algebra, probability and calculus as actually used.
Math for AI is taught here in 3 lessons, running from Vectors and matrices for ML through to Gradients and calculus intuition. Each lesson takes one topic, shows the working code, and links onward to the next, so the course can be read straight through in order.
| Track | AI & Intelligent Development |
| Lessons | 3 |
| Level | Beginner |
| Reading time | about 39 minutes |
| Prerequisites | Helpful, but not required: AI Agents |
Lessons
- Vectors and matrices for MLThe two objects that every model is built from: vectors as features, matrices as batched linear maps, and the shape rules that decide whether your code runs.
- Probability and distributionsRandom variables, the distributions ML actually uses, and how cross-entropy is just negative log likelihood in disguise.
- Gradients and calculus intuitionDerivatives as sensitivity, the chain rule behind backpropagation, and how to tell a learning-rate problem from an architecture problem.
More in AI & Intelligent Development
AI Basics AI Agents Machine Learning scikit-learn TensorFlow PyTorch Natural Language Processing LangChain Ollama OpenCV Codex Claude Code OpenCode Vibe Coding Selenium Playwright
FAQ
How long does the Math for AI course take?
It has 3 lessons, about 39 minutes of reading. Expect roughly twice that if you type out and run every example.
Do I need prior experience for Math for AI?
Not strictly. It helps to have read AI Agents first, because some lessons build on it, but every lesson explains its own assumptions.
What should I read after Math for AI?
Continue with Machine Learning (13 lessons), the next course in AI & Intelligent Development.
Last refreshed 2026-09-18.