The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building Python programs.

As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read further to the following main chapters. Those succeeding chapters present essential topics in statistical learning: linear regression, classification, resampling, information criteria, regularization, nonlinear regression, decision trees, support vector machines, and unsupervised learning. 

Each chapter mathematically formulates and solves machine learning problems and builds the programs. The body of a chapter is accompanied by proofs and programs in an appendix, with exercises at the end of the chapter. Because the book is carefully organized to provide the solutions to the exercises in each chapter, readers can solve the total of 100 exercises by simply following the contents of each chapter.

This textbook is suitable for an undergraduate or graduate course consisting of about 12 lectures. Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning.

机器学习和数据科学最关键的能力是把握其本质的数学逻辑，而不是知识和经验。这本教科书通过考虑数学问题和建立Python程序来接近机器学习和数据科学的本质。

作为初始部分，第1章对线性代数进行了简明的介绍，这将有助于新手进一步阅读下面的主要章节。接下来的那些章节介绍了统计学习的基本主题：线性回归、分类、重采样、信息准则、正则化、非线性回归、决策树、支持向量机和无监督学习。

每一章都从数学角度阐述和解决机器学习问题，并建立程序。一章的正文在附录中附有证明和程序，并在章末附有练习。由于本书精心组织了每一章的习题解答，读者只需按照每一章的内容就可以解决总共100道习题。

本教材适用于由约12个讲座组成的本科生或研究生课程。本书以简单易懂、自成一体的风格编写，也将是独立学习的完美材料。