Welcome to the future home of the official code repository for my upcoming book, "Mathematics for Machine Learning"!
This book is being written for aspiring machine learning engineers, data scientists, and developers who want to move beyond just using libraries and truly understand the mathematical engines that power modern AI. My goal is to create the most intuitive, practical, and code-driven guide to the essential math you need to succeed.
This will not be a dry, academic textbook. Every mathematical concept will be tied directly to a practical machine learning algorithm, explained with visual intuition, and implemented from scratch in Python.
- Part 1: The Language of Data - Core Linear Algebra (Vectors, Matrices, PCA)
- Part 2: The Engine of Learning - Essential Calculus (Gradients, Gradient Descent, Backpropagation)
- Part 3: Quantifying Uncertainty - Practical Probability & Statistics (Bayes' Theorem, Distributions, Bias-Variance Tradeoff)
- Part 4: Building from Scratch - Complete, from-scratch implementations of Linear Regression, Logistic Regression, K-Means, and a simple Neural Network.
This project is in the early stages of writing. This README will be updated with the full code structure and setup instructions as the book's development progresses. Thank you for your interest!