Master the Mathematics,
Behind Machine Learning
& AI
Build true intuition with visual explanations, step-by-step derivations, and interactive tools. Learn Linear Algebra, Calculus, Probability, Optimization, and the mathematical foundations that power modern ML.
Trust Signal: Beginner to Advanced Content
Essential Math for Machine Learning & AI Curriculum
Browse individual modules to learn specific topics and strengthen your math understanding.
Math Foundations
Algebra, vectors, matrices, functions, and basic calculus for building a strong base.
Explore Modules →Linear Algebra for ML
Matrices, eigenvalues, SVD, projections, and geometry used in modern ML algorithms.
Explore Modules →Calculus & Optimization
Derivatives, gradients, multivariable calculus, and optimization methods like gradient descent.
Explore Modules →Probability for ML
Random variables, distributions, Bayes rule, expectation, variance, and ML-focused statistics.
Explore Modules →Statistics for ML
The math behind regression, loss functions, regularization, PCA, softmax, and other ML concepts.
Explore Modules →Other Math
Explore concepts interactively—matrix operations, gradient descent visualizer, PCA explorer, and more.
Explore Modules →Latest from mathforml
Fresh insights, visual explanations, and project deep dives.
- Why PCA Works: An Eigenvalue Story of Dimensionality
Why PCA Works: An Eigenvalue Story of DimensionalityWhy PCA Works: An Eigenvalue Story of Dimensionality - Transformers Explained Visually: Attention is All You Need
Transformers Explained Visually: Attention is All You Need - How Gradient Descent Really Works: A Geometric Intuition
Maths for Machine Learning Maths for Machine Learning