MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02660nam a2200229Ia 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
NULRC |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20250520102834.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
250520s9999 xx 000 0 und d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781118961742 |
| 040 ## - CATALOGING SOURCE |
|---|
| Transcribing agency |
NULRC |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
Q 325.5 .B69 2015 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Bowles, Michael |
| Relator term |
author |
| 245 #0 - TITLE STATEMENT |
|---|
| Title |
Machine learning in Python : |
| Remainder of title |
essential techniques for predictive analysis / |
| Statement of responsibility, etc. |
Michael Bowles |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Indianapolis, Indiana : |
| Name of publisher, distributor, etc. |
John Wiley & Son, Inc., |
| Date of publication, distribution, etc. |
c2015 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xxix, 326 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
24 cm. |
| 365 ## - TRADE PRICE |
|---|
| Price amount |
PHP1746.78 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes index. |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
1. The two essential algorithms for making predictions -- 2. Understand the problem by understanding the data -- 3. Predictive model building: balancing performance, complexity, and big data -- 4. Penalized linear regression -- 5. Building predictive models using penalized linear methods -- 6. Ensemble methods -- 7. Building ensemble models with Python. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, data preparation, and using the trained models in practice. You will learn a core set of Python programming techniques, various methods of building predictive models, and how to measure the performance of each model to ensure that the right one is used. The chapters on penalized linear regression and ensemble methods dive deep into each of the algorithms, and you can use the sample code in the book to develop your own data analysis solutions. Machine learning algorithms are at the core of data analytics and visualization. In the past, these methods required a deep background in math and statistics, often in combination with the specialized R programming language. This book demonstrates how machine learning can be implemented using the more widely used and accessible Python programming language. Machine learning doesn't have to be complex and highly specialized. Python makes this technology more accessible to a much wider audience, using methods that are simpler, effective, and well tested. Machine Learning in Python shows you how to do this, without requiring an extensive background in math or statistics. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
MACHINE LEARNING |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Books |
