Python for the Mathematics of Machine Learning: Master Linear Algebra, Calculus, and Statistics in Python to Build Powerful

Do you want to understand the mathematics behind machine learning and learn how to apply it with Python?

This book makes it easy. Python for the Mathematics of Machine Learning is a practical guide that takes you step by step through the linear algebra, calculus, and statistics you need to build and understand powerful machine learning models.

You don’t need to be a math expert or an experienced programmer. Using clear explanations, real-world examples, and hands-on code, you’ll quickly master the essential concepts and see how they work in practice.

Inside, you’ll discover how to:

Work with vectors, matrices, eigenvalues, and decompositions using NumPy and SciPy

Understand derivatives, gradients, and optimization techniques to improve model performance

Apply probability and statistics to analyze data and make better predictions

Build machine learning models from scratch and with scikit-learn

Avoid common mistakes beginners make when learning math for machine learning

Whether you’re a student, self-learner, or professional looking to strengthen your skills, this book gives you the math foundations for AI, data science, and deep learning—all taught with practical Python examples.

If you’ve struggled to connect the dots between math theory and machine learning practice, this guide shows you exactly how to do it.

Get your copy of Python for the Mathematics of Machine Learning today and start building smarter models with confidence.