ALL

I want to learn Everything

The full curriculum in the recommended order. Become a math-for-AI expert.

29 lessons 1860 XP

Linear Algebra for Machine Learning

Vectors and Vector Operations

Learn what vectors are, why they are the fundamental building blocks of machine learning, and how to manipulate them.

Matrices and Matrix Operations

Understand matrices as the core data structure of machine learning — from storing datasets to representing neural network layers.

+60 XP

Matrix Multiplication and Its Meaning

Go deep on matrix multiplication — the single most important operation in all of AI. Understand what it really does and why it matters.

+70 XP

Transpose and Inverse of Matrices

Coming soon

Systems of Linear Equations

Coming soon

Vector Spaces and Subspaces

Coming soon

Linear Transformations

Coming soon

Eigenvalues and Eigenvectors

Coming soon

Singular Value Decomposition

Coming soon

Principal Component Analysis

Coming soon

Calculus for Deep Learning

Functions and Their Graphs

Coming soon

Derivatives and What They Mean

Coming soon

The Chain Rule

Coming soon

Partial Derivatives

Coming soon

Gradients and Gradient Vectors

Coming soon

Jacobian and Hessian Matrices

Coming soon

Probability & Statistics for ML

Basic Probability Rules

Coming soon

Conditional Probability

Coming soon

Bayes Theorem

Coming soon

Common Probability Distributions

Coming soon

Expected Value and Variance

Coming soon

Maximum Likelihood Estimation

Coming soon

Optimization for AI

What is Optimization?

Coming soon

Loss Functions and Cost Functions

Coming soon

Gradient Descent

Coming soon

Information Theory for AI

Entropy: Measuring Information

Coming soon

Cross-Entropy and KL Divergence

Coming soon

Discrete Math Essentials for AI

Graph Theory Basics

Coming soon

Basic Combinatorics

Coming soon