Courses
Six focused tracks covering only the mathematics you need for AI and machine learning.
LA
Linear Algebra for Machine Learning
The language of AI. Vectors, matrices, transformations — everything your neural network is built from.
10 lessons3 chapters
Chapter 1: Foundations: Vectors and Matrices
Chapter 2: Linear Transformations
Chapter 3: Advanced: Eigen-everything and Decompositions
CA
Calculus for Deep Learning
Derivatives, gradients, and the chain rule — the math that makes neural networks learn.
6 lessons2 chapters
Chapter 1: Foundations: Functions and Derivatives
Chapter 2: Multivariable Calculus for Neural Networks
PR
Probability & Statistics for ML
Probability distributions, Bayes theorem, and statistical thinking for machine learning.
6 lessons2 chapters
Chapter 1: Probability Foundations
Chapter 2: Probability Distributions
OP
Optimization for AI
Gradient descent, loss functions, and the engine that trains every ML model.
3 lessons1 chapters
Chapter 1: Optimization Foundations
IT
Information Theory for AI
Entropy, cross-entropy, and KL divergence — the math behind language models and classification.
2 lessons1 chapters
Chapter 1: Information Theory Essentials
DM
Discrete Math Essentials for AI
Graph theory and combinatorics — the math behind graph neural networks.
2 lessons1 chapters