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From Basic Algebra to Information Theory. Master every mathematical concept behind Machine Learning.
Equations, logarithms, summation notation — the language of ML formulas.
Vectors, matrices, transformations, eigenvalues — the backbone of ML.
Derivatives, partial derivatives, chain rule, integrals for optimization.
Distributions, Bayes theorem, expected value — reasoning under uncertainty.
Descriptive stats, hypothesis testing, MLE — making data-driven decisions.
Broadcasting, norms, SVD, and the matrix math behind every ML pipeline.
Gradient vectors, Jacobians, Hessians — the math powering backpropagation.
Convexity, gradient descent variants, Lagrange multipliers, Adam optimizer.
Level 0 — Foundation
Algebra → Linear Algebra → Calculus → Probability → Statistics
Can be learned in parallel. Start with any.
Level 1 — ML-Core
Requires Foundation. Matrix Ops + Derivatives + Optimization
Essential for understanding any ML algorithm.
Level 2 — Advanced
Requires Level 1. Eigen + Info Theory for deep learning.
Powers PCA, loss functions, and modern architectures.