Back

This course builds genuine intuition for the linear algebra, calculus, and probability that power machine learning — through code, visualization, and real ML model examples.

✅ What’s Inside:

  1. Linear Algebra Essentials
  2. Matrices as Transformations
  3. Eigenvalues in PCA
  4. Calculus for Backpropagation
  5. Gradient Descent Geometry
  6. Probability Theory Basics
  7. Bayesian Thinking
  8. Information Theory for NLP
  9. Optimization Landscape
  10. Regularization Mathematics
  11. Statistical Testing for ML
  12. Project: Implement Backprop from Scratch