Description

Mathematics for Machine Learning is one of the most popular modern math books for students entering artificial intelligence, data science, and machine learning.

It is designed specifically to bridge the gap between basic undergraduate math and the mathematical tools used in ML algorithms.

What the book covers

The book focuses on the key mathematical foundations used in machine learning:

• linear algebra (vectors, matrices, eigenvalues)
• analytic geometry
• multivariate calculus
• partial derivatives and gradients
• probability theory and distributions
• Bayesian reasoning basics
• optimization (gradient descent and variants)

Why it is in high demand

This book became highly popular because of the rapid growth of AI and data science. It is widely used in:

• machine learning bootcamps
• computer science programs
• self-study AI learners
• data science courses

It is especially valued because it explains why the math works, not just formulas.