Mathematics of Machine Learning
Master the core math of machine learning and apply it with Python to solve real-world data challenges.
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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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- Online reading access
- 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 amazing at balancing mathematical depth with real-world machine learning intuition. Tivadar Danka delivers a rare combination of clarity and engineering mathematical relevance that is required for any data professional to succeed. From linear algebra to probability, each chapter builds the mathematical fluency needed to move from using ML tools to truly understanding them.
- Cornellius Y. Data Science Lead and Startup Founder (Arif Analytics)
Check out the book’s code and datasets on GitHub at [GitHub - cosmic-cortex/mathematics-of-machine-learning-book ]. Feel free to fork and star if you find it useful.
Meet the Author: Live AMA with Tivadar
“Mathematics of Machine Learning” is a clear, structured guide to the core math powering modern ML, linear algebra, calculus, and probability, all tied to real-world applications. Whether you're a student or pro, it’s a must-read for building a solid, intuitive foundation in ML theory and practice.
- Etibar Aliyev, AI Expert & Leader at Google
I used to build ML models without fully understanding the math behind them, often running into issues I couldn’t quite explain. I just went through a resource that finally breaks it all down, clear, intuitive, and hands-on. If you're working in AI/ML and still find the math a bit fuzzy, this is definitely worth your time.
- Alex Razvant, Senior AI Engineer | Founder at NeuralBits
As a professor teaching AI, I’m always looking for resources that combine mathematical rigor with practical relevance—and Mathematics of Machine Learning delivers. I plan to assign several chapters as required readings in my AI course, especially those on linear algebra and multivariable calculus. Tivadar Danka does an excellent job connecting core mathematical concepts to real-world AI applications, with clear Python examples that make the material approachable. I also highly recommend this book to professionals looking to strengthen their mathematical foundation for AI through self-study—it’s both structured and accessible.
- Prof. Tom Yeh, University of Colorado Boulder, AI by Hand ✍️
"Surprisingly Good. Actually, Shockingly Good."
Most books that claim to teach the math behind machine learning either oversimplify or drown you in abstraction. This one does neither. It’s precise where it matters, informal when it helps, and never loses sight of why the math exists in the first place.
No fluff, no cringe analogies - just clear thinking and beautifully structured exposition. You can tell the author has done the proofs and written the code.
Highly recommend it for anyone serious about understanding, not just using, ML.
- Mike Erlihson, PhD, Head of AI at Stealth startup>