Data science and machine learning have deeply integrated into various aspects of our lives, and mathematics is the key to unlocking the door to the future. Not everyone is born with a good hand, but mastering "mathematics + programming + machine learning" is definitely a trump card. This time, learning mathematics is no longer just for exams, grades, or further education, but an investment in time, self-realization, and future-oriented. In order to make everyone learn and even fall in love with mathematics, the author of this book tries to overcome the various drawbacks of traditional mathematical textbooks, making the learning process interesting, understandable, thought-provoking, more confident, and applicable.This book breaks through the barriers between different mathematical fields and combines arithmetic, algebra, linear algebra, geometry, analytic geometry, probability statistics, calculus, optimization methods, and other subjects together. Starting from addition, subtraction, multiplication, and division, the main content includes: Chapters 1 and 2 explain the basic operations of vectors and matrices; Chapter 3 introduces common geometric knowledge; Chapter 4 explains algebraic knowledge; Chapters 5 and 6 introduce coordinate systems; Chapters 7, 8, and 9 focus on analytic geometry; Chapters 10 to 14 revolve around functions; Chapters 15 to 19 explain calculus and optimization problems; Chapters 20 and 21 are an introduction to probability and statistics; and the last four chapters of the book conclude with linear algebra.The content of this book emphasizes "illustration + programming + machine learning applications". When explaining specific mathematical tools, the book will also discuss their applications in the field of data science and machine learning, enabling readers to apply what they learn.This book is marketed as covering "from arithmetic to machine learning", but it is recommended that readers have at least a high school level of mathematics knowledge. If readers are currently studying or have studied university-level mathematics (calculus, linear algebra, probability statistics), it will be even easier to read.The target audience of this book includes anyone who applies mathematics in their work, especially suitable for junior programmers looking to advance, undergraduate students seeking a better understanding of mathematics, senior data analysts, and artificial intelligence developers.