Linear algebra, calculus, probability, statistics, and optimization — the mathematical foundation for all AI.
- 3Blue1Brown: Essence of Linear Algebra - Beautiful visual series that builds deep intuition for linear algebra.
Beginner - MIT 18.06: Linear Algebra (Gilbert Strang) - The classic MIT linear algebra course — complete with lectures, notes, and exams.
Beginner - Interactive Linear Algebra (GT) - Free interactive online textbook from Georgia Tech.
Beginner
- 3Blue1Brown: Essence of Calculus - Visual series that makes calculus intuitive and enjoyable.
Beginner - MIT 18.01/18.02: Single & Multivariable Calculus - Complete MIT calculus courses with lectures and problem sets.
Beginner - Convex Optimization (Boyd & Vandenberghe) - Stanford's free textbook on the optimization methods used in ML.
Advanced
- Khan Academy: Statistics & Probability - Comprehensive, interactive lessons from the ground up.
Beginner - StatQuest (YouTube) - Intuitive, entertaining explanations of statistics and ML math.
Beginner - Harvard: Statistics 110 (Probability) - Full probability course with video lectures and practice problems.
Intermediate - MIT 18.650: Statistics for Applications - Statistical methods used in data science and ML.
Intermediate
- Mathematics for Machine Learning (Book) - Free textbook covering linear algebra, calculus, probability, and optimization for ML.
Intermediate - Coursera: Mathematics for Machine Learning Specialization - Imperial College London specialization covering linear algebra, multivariate calculus, and PCA.
Beginner - Seeing Theory (Brown University) - Interactive visualizations that make probability and statistics intuitive.
Beginner - Matrix Calculus for Deep Learning (Explained.ai) - Free paper explaining matrix calculus specifically for deep learning.
Intermediate