| 000 -LEADER |
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| fixed length control field |
a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
250306b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9789811578762 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
519.5 |
| Item number |
SUZ |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Suzuki, Joe |
| 245 ## - TITLE STATEMENT |
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| Title |
Statistical learning with math and Python : 100 exercises for building logic |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc |
Springer, |
| Date of publication, distribution, etc |
2021 |
| Place of publication, distribution, etc |
Singapore : |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xi, 256 p. ; |
| Other physical details |
ill. (some col.), |
| Dimensions |
24 cm |
| 365 ## - TRADE PRICE |
|---|
| Price amount |
35 |
| Price type code |
€ |
| Unit of pricing |
93.20 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes index. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Mathematical statistics |
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| Topical term or geographic name as entry element |
Symbolic and mathematical |
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| Topical term or geographic name as entry element |
Computer program language |
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| Topical term or geographic name as entry element |
Decision tree |
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| Topical term or geographic name as entry element |
Data frame |
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| Topical term or geographic name as entry element |
Eigenvalues |
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| Topical term or geographic name as entry element |
Inner product; |
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| Topical term or geographic name as entry element |
Linear regression |
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| Topical term or geographic name as entry element |
Orthogonal matrix |
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| Topical term or geographic name as entry element |
Prediction intervals |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
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| Item type |
Books |