Math for Machine Learning
The math & statistics that ML stands on — distributions, Bayes, linear algebra, and calculus — as interactive visualizations plus a written handbook.
Who it's for
Learners who want the mathematical foundations behind ML to actually click.
What you'll learn
- Read and reason about probability distributions
- Apply Bayes' theorem with intuition, not just formulas
- Understand vectors, matrices, and gradients as ML uses them
- Connect the math directly to ML algorithms
Course contents
- 01Probability & statistics — distributions, Bayes, sampling
- 02Linear algebra — vectors, matrices, projections
- 03Calculus — derivatives, gradients, optimization
- 04Tying it to ML