Mathematical Foundations
The language every ML algorithm speaks
⬡ 3 topics · 9 chapters · ◷ ~2.2 hrs
Topics
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01
Vectors
Direction, magnitude, and the arrows that describe ML
foundational ◷ 51 min ≡ 3 chapters
01 What Is a Vector?
02 Vector Operations
03 Vector Spaces and Basis
02
Matrices and Transformations
Linear maps that rotate, scale, shear, and reflect
foundational ◷ 41 min ≡ 3 chapters
01 What Does a Matrix Do?
02 Matrix Multiplication
03 Eigenvectors
03
Derivatives and Gradients
The rate of change at a point — and why it drives every training loop
intermediate ◷ 40 min ≡ 3 chapters
01 The Derivative
02 The Gradient
03 The Chain Rule