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Module · 7 lessons
Math Foundations for ML
Build the calculus and linear algebra intuition that every machine learning algorithm relies on.
At a glance
Level
Beginner
Lessons
7 lessons
Time to complete
1–2 weeks
Cost
Free forever · no sign-up
Machine learning is built on a small set of mathematical ideas. Before you train a single model, this module gives you the calculus and linear algebra you need to understand why algorithms work the way they do — from the derivatives behind gradient descent to the vectors and matrices that represent data.
Lessons in this module
1
Understanding Linear and Nonlinear Functions
Learn how functions describe relationships in data, and how linear functions differ from nonlinear ones, the foundation for all of machine learning math
2
Understanding Limits
Learn what a limit is and how a function can approach a value even where it is undefined, using a real worked example in NumPy
3
Derivatives and Finding Extreme Points
Learn derivatives as instantaneous slope, the rules of differentiation, and how to find a function's extreme points where the derivative is zero
4
Linear Systems
Learn what a system of linear equations is, how its solution is a point of intersection, and how to solve one in NumPy
5
Vectors
Understand vectors as the core data structure of machine learning: representation, addition, scaling, the dot product, and magnitude, all in NumPy
6
Matrix Algebra
Learn matrix multiplication and the geometric view of matrices as linear transformations using NumPy
7
Solution Sets and Linear Independence
Understand solution sets, span, and linear independence, and compute matrix rank with NumPy to see when vectors span a plane versus only a line.
Achievement
Complete all 7 lessons to finish the Math Foundations for ML module.