Overview
- Equips readers with the logic required for machine learning and data science via math and programming
- Provides in-depth understanding of Python source programs rather than how to use ready-made Python packages
- Written in an easy-to-follow and self-contained style
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Table of contents (11 chapters)
Keywords
About this book
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.
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Statistical Learning with Math and Python
Book Subtitle: 100 Exercises for Building Logic
Authors: Joe Suzuki
DOI: https://doi.org/10.1007/978-981-15-7877-9
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
Softcover ISBN: 978-981-15-7876-2Published: 04 August 2021
eBook ISBN: 978-981-15-7877-9Published: 03 August 2021
Edition Number: 1
Number of Pages: XI, 256
Number of Illustrations: 1 b/w illustrations
Topics: Artificial Intelligence, Machine Learning, Statistics and Computing/Statistics Programs, Computational Intelligence, Data Structures and Information Theory