Mathematics of Machine Learning provides a rigorous yet accessible introduction to the mathematical underpinnings of machine learning, designed for engineers, developers, and data scientists ready to elevate their technical expertise. With this book, you’ll explore the core disciplines of linear algebra, calculus, and probability theory, essential for mastering advanced machine learning concepts.
The book balances theory and application, offering clear explanations of mathematical constructs and their direct relevance to machine learning tasks. Through practical Python examples, you’ll not only learn the mathematics but also how to implement and use these ideas in real-world scenarios, such as optimizing algorithms or solving specific challenges in neural network training.
Whether you aim to deepen your theoretical knowledge or enhance your capacity to solve complex machine learning problems, this book provides the structured guidance you need. By the end of this book, you’ll gain the confidence to engage with advanced machine learning literature and tailor algorithms to meet specific project requirements.

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Spis treści

Mathematics of Machine Learning. Master Linear Algebra, Calculus, and Probability for Machine Learning eBook -- spis treści

• 1. Vectors and vector spaces
• 2. The geometric structure of vector spaces
• 3. Linear algebra in practice spaces: measuring distances
• 4. Linear transformations
• 5. Matrices and equations
• 6. Eigenvalues and eigenvectors
• 7. Matrix factorizations
• 8. Matrices and graphs
• 9. Functions
• 10. Numbers, sequences, and series
• 11. The structure of real numbers
• 12. Differentiation
• 13. Integration
• 14. Optimization
• 15. Multivariable functions
• 16. Derivatives and gradients
• 17. Optimization in multiple variables
• 18. What is probability?
• 19. Random variables and distributions
• 20. The expected value
• 21. The maximum likelihood estimation
• 22. It's just logic
• 23. The structure of mathematics
• 24. Basics of set theory
• 25. Complex numbers