ML Foundations
The Mathematical Backbone of Every ML Algorithm
Linear algebra, calculus, and probability form the foundation of all machine learning. Master these concepts to truly understand how algorithms work.
- Linear Algebra — Vectors, matrices, and the language of data
- Calculus — Derivatives and gradient descent for optimization
- Probability and Statistics — Bayes' theorem, distributions, and inference
"Mathematics is the language in which God has written the universe."
Math Foundations for Machine Learning
Math is the language of machine learning. This tutorial covers the essential math you need — with clear explanations, visual intuitions, and Python code.
Linear Algebra
Vectors and Vector Operations
Matrices
Matrix Operations in ML
Calculus
Derivatives and Gradients
Gradient Descent
Partial Derivatives and the Gradient
Chain Rule
Probability
Probability Axioms
Conditional Probability and Bayes' Theorem
Distributions
Expectation and Variance
Key Takeaways
What to Learn Next
-> What is Machine Learning? The complete introduction to ML — concepts, types, and workflow.
-> Linear Regression From scatter plots to predictions — the simplest ML algorithm.
-> Logistic Regression Classification with probability — from linear to sigmoid.
-> Dimensionality Reduction Reduce features while preserving information with PCA and t-SNE.
-> Regularization Prevent overfitting with Ridge, Lasso, and Elastic Net.
-> KNN Instance-based learning where your neighbors tell the story.