Last updated: Feb 28, 2026

Machine learning relies heavily on mathematical foundations. A practical breakdown of the math used in ML is roughly:

• 50% Probability & Statistics
• 35% Linear Algebra
• 15% Calculus

Below are high-quality resources to build each part of this foundation.

📍See below for a list of topics to study

🎥 Video-Based Learning (If You Prefer YouTube)

• StatQuest – Statistics intuition
• 3Blue1Brown – Linear algebra playlist
• Khan Academy – Statistics, Linear Algebra, and Calculus
• Andrej Karpathy: Zero to Hero – Neural networks from scratch

Probability

A strong starting point is the Introduction to Probability book by Harvard professors.

• Free PDF:

  https://drive.google.com/file/d/1VmkAAGOYCTORq1wxSQqy255qLJjTNvBI/edit

• Animated EdX course (follows the book):

  https://www.edx.org/learn/probability/harvard-university-introduction-to-probability

Key topics covered:

• Counting