Mathematics of Machine Learning  

Master the core math of machine learning and apply it with Python to solve real-world data challenges.

Enter your email to receive Essential Math for Machine Learning within 24 hours, your free primer on the key concepts behind the book. 

What's Inside

Learn the essential mathematics behind modern machine learning with clear explanations and practical guidance. 

This book offers a comprehensive discourse on the core mathematical concepts – linear algebra, calculus, and probability – that form the foundation of ML algorithms.

Authored by Tivadar Danka, an experienced machine learning engineer and educator, the book bridges the gap between abstract theory and real-world application. 

You’ll explore the math behind key machine learning techniques and learn to implement them effectively using hands-on Python examples.

Whether you’re a data scientist, analyst, researcher, or aspiring ML practitioner, you’ll gain the skills to:

About The Author

Tivadar Danka is a mathematician by training, a machine learning engineer by profession, and an educator by passion. Since earning his PhD in 2016, he has focused on applying deep learning to real-world problems, from analyzing microscopy images to building popular open-source tools. Tivadar is also known for creating high-quality educational content and has built a following of over 100,000 on social media.

You can purchase the full title from Packt. Buying direct gives you access to:

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• AI Assistant to answer your questions as you read along

Sign up to receive a free precursor that builds your foundation in the core math powering machine learning, including linear algebra, calculus, and probability.

• Understand and apply core mathematical principles used in ML
• Decode the theory behind essential algorithms
• Customize and optimize your models for better performance
• Strengthen your foundations to tackle advanced ML topics

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What the Reviewers Say 

“This book is a must-read for every Machine Learning Engineer. It’s the math-first roadmap I wish I had when I started — practical, intuitive, and production-focused. I remember diving into ML models without fully understanding the math behind them and struggling when things broke or didn’t scale. If only I had this book back then. It bridges the gap between theory and real-world ML engineering. Start here if you want to understand the ‘why’ behind the code.”

— Cornellius Y.
DS, ML & AI Specialist | Influencer & Career Mentor