Learn about Math for Machine Learning with interactive visualizations and depth.
Course Modules
Linear Algebra Basics
Master the fundamentals of linear algebra for machine learning, including scalars, vectors, matrices, tensors, basic operations, and applications. Learn it!
Calculus Fundamentals
Master the core concepts of Calculus for Machine Learning: Derivatives, Multivariable Gradients, Chain Rule, Gradient Descent Algorithms, and Taylor Series.
Probability & Statistics
Master the probability theory behind Machine Learning. From Bayes' Theorem to Hypothesis Testing and Naive Bayes Classifiers for machine learning pipelines.
Advanced Optimization
Master the algorithms that power Deep Learning models. From Convexity to Backpropagation, and why Adam is the default optimizer for modern networks today.
Advanced Linear Algebra
Master Advanced Linear Algebra for ML: Eigenvalues, PCA, Matrix Decompositions, Tensors, Jacobians, and Neural Network Applications.
Discrete Math & Information Theory
Master Discrete Math & Information Theory: Entropy, Graph Theory, Fourier Transforms, and Complex Numbers for AI. Plus a deeper dive into Transformers.