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From Basic Algebra to Information Theory. Master every mathematical concept behind Machine Learning.
5 Modules
Foundation
3 Modules
ML-Core
2 Modules
Advanced
50
Total Topics
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.
Understand Models
Know WHY algorithms work, not just how to call them.
Crack Interviews
Top companies ask derivations of gradient descent, PCA, etc.
Debug Faster
When loss explodes, math tells you exactly what went wrong.
Research Papers
Read and implement cutting-edge papers without confusion.