1. Introduction

This introduction introduces the overall background and purpose of the materials covered in this book.

It examines the importance of statistical learning and major datasets, and guides you through the prerequisite knowledge necessary for learning.

1.1 An Overview of Statistical Learning

This section provides an overview of what Statistical Learning is, exploring it through various examples and problems.

It suggests the basic direction of how statistical methods are utilized in modern data analysis.

1.2 A Brief History of Statistical Learning

It covers a brief history focusing on major milestones of how statistics and machine learning have evolved from the past to the present.

You can understand the historical context in which the data science field was formed and the background behind the emergence of major methodologies.

1.3 This Book

It explains the purpose for which this book was written, and what distinctions and characteristics it has compared to other textbooks.

It notes that the focus is on a practical approach and application using Python rather than deep theoretical depth.

1.4 Who Should Read This Book?

It defines the target audience of this textbook with respect to academic backgrounds and interests.

It explains that this book is suitable for industry practitioners and non-majors who want to apply data modeling to practice, even without advanced mathematical knowledge.

1.5 Notation and Simple Matrix Algebra

It summarizes the statistical and mathematical notation and basic vector and matrix operation rules that will be used throughout the book.

It provides clear rules on how to mathematically represent data structures for readers who are not familiar with formulas.

1.6 Organization of This Book

It summarizes the main topics of each chapter included in this book and their logical connections at an overview level.

You can view the learning roadmap and flow you will obtain as you read sequentially from beginning to end.

1.7 Data Sets Used in Labs and Exercises

It introduces the types and sources of major datasets that will be continuously utilized in Python labs and chapter exercises.

You can confirm that data from various domains collected in the real world, such as healthcare, economics, and marketing, are being used.

1.8 Book Website

It guides you to the official website that provides dataset downloads and supplementary materials, as well as offline resources related to this textbook.

It specifies the online channel where readers can check the latest Python code and learn additional lab content.

1.9 Acknowledgements

This is a word of gratitude to fellow researchers, reviewers, and contributors who provided academic and technical assistance until this textbook was published.

It honors the contributions of many eminent scholars and the community who have advanced statistical learning theory together.

Sub-Chapters

1.1 An Overview Of Statistical Learning >

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