Mathematics of Machine Learning: Master Linear Algebra, Calculus, and Probability for Machine Learning

Master the math behind machine learning algorithms with this comprehensive guide to linear algebra, calculus, and probability, complete with Python examples Key Features Master linear algebra, calculus, and probability theory for ML Understand mathematical structures behind machine learning algorithms Learn Python implementations of core mathematical concepts Develop skills to optimize, customize, and analyze machine learning solutions Bridge the gap between theory and real-world applications Book DescriptionMathematics 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.What you will learn Core concepts of linear algebra, including matrices, eigenvalues, and decompositions Fundamental principles of calculus, including differentiation and integration Advanced topics in multivariable calculus for optimization in high dimensions Essential probability concepts like distributions, Bayes' theorem, and entropy Python-based implementations to bring mathematical ideas to life The superpower of mathematical and scientific thinking, with applications to data science and machine learning Who this book is forThis book is for aspiring and practicing machine learning engineers, data scientists, and software developers who wish to gain a deeper understanding of the mathematics that drives machine learning. A foundational understanding of Python and a basic familiarity with machine learning tools are recommended.

Table of Contents:
Table of Contents Vectors and vector spaces The geometric structure of vector spaces Linear algebra in practice spaces: measuring distances" Linear transformations Matrices and equations Eigenvalues and eigenvectors Matrix factorizations Matrices and graphs Functions Numbers, sequences, and series The structure of real numbers Differentiation Integration Optimization Multivariable functions Derivatives and gradients Optimization in multiple variables What is probability? Random variables and distributions The expected value The maximum likelihood estimation It's just logic The structure of mathematics Basics of set theory Complex numbers