Master the mathematics driving the revolution in artificial intelligence! This book is an ideal guide for anyone who wants to understand the fundamental and advanced methods of machine learning through mathematical concepts.

Mathematics for Machine Learning offers a comprehensive, self-contained explanation of all the key mathematical topics needed to understand and effectively use machine learning methods. With minimal prerequisite knowledge, it guides you through areas such as linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization and probability and statistics. This enables you to confidently and systematically advance your ability to work with data and build your own machine learning models.

Thanks to its clear and cohesive approach, this book is suitable not only for students and beginners, but also for more advanced users who wish to strengthen and expand the mathematical foundations required for the successful deployment and understanding of modern algorithms.

Why choose this book?

• Comprehensive overview: covers all essential mathematical concepts related to machine learning.
• No unnecessary prerequisites: suitable for readers with minimal mathematical background.
• Clear explanations: learn the mathematics step by step, making complex topics easier to understand.
• Practical application: theoretical knowledge can be readily applied to your own machine learning projects.
• Long-term benefit: a broadened and solid mathematical foundation will ease further study and work in data science and AI.

Key features of the book

• Includes topics such as linear algebra, analytic geometry and vector calculus.
• Explains matrix decomposition methods that are essential for effective data work.
• Covers the basics of optimization, which are key to training models.
• Provides advanced knowledge of probability and statistics, important for data interpretation.
• Enables independent study thanks to a clear and complete presentation structure.