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 series tries to overcome the various drawbacks of traditional mathematical textbooks, making the learning process interesting, understandable, thought-provoking, more confident, and applicable.The book series is divided into three main sections: programming, mathematics, and practice. Various algorithms of data science and machine learning cannot be separated from mathematics. "Matrix Power," as the second book of the "Mathematics" section, mainly introduces commonly used linear algebra tools. Any mathematical tool that wants to expand from univariate to multivariate, such as multivariate calculus and multivariate statistics, cannot avoid linear algebra.The book consists of 25 chapters and can be classified into seven main sections: vectors, matrices, vector spaces, matrix decomposition, calculus, spatial geometry, and data.When explaining linear algebra tools, the book will also discuss their applications in the fields of data science and machine learning, enabling readers to apply what they learn.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.