Statistical Learning with Math and Python: 100 Exercises for Building Logic by J

Description: Statistical Learning with Math and Python by Joe Suzuki 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. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description 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. Back Cover 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. Author Biography Joe Suzuki is a professor of statistics at Osaka University, Japan. He has published more than 100 papers on graphical models and information theory. Table of Contents Chapter 1: Linear Algebra.- Chapter 2: Linear Regression.- Chapter 3: Classification.- Chapter 4: Resampling.- Chapter 5: Information Criteria.- Chapter 6: Regularization.- Chapter 7: Nonlinear Regression.- Chapter 8: Decision Trees.- Chapter 9: Support Vector Machine.- Chapter 10: Unsupervised Learning. Feature 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 Details ISBN9811578761 Author Joe Suzuki Short Title Statistical Learning with Math and Python Language English Year 2021 ISBN-10 9811578761 ISBN-13 9789811578762 Format Paperback Subtitle 100 Exercises for Building Logic DOI 10.1007/978-981-15-7877-9 Publisher Springer Verlag, Singapore Edition 1st Imprint Springer Verlag, Singapore Place of Publication Singapore Country of Publication Singapore Pages 256 Publication Date 2021-08-04 UK Release Date 2021-08-04 Edition Description 1st ed. 2021 DEWEY 006.31 Audience Undergraduate Illustrations 1 Illustrations, black and white; XI, 256 p. 1 illus. We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:134286750;

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