MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02267nam a22002177a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20211208152903.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
211208b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9789352134571 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
005.133 |
| Item number |
MUL |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Müller, Andreas C. |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Introduction to machine learning with Python : |
| Remainder of title |
a guide for data scientists |
| Statement of responsibility, etc. |
Andreas C Müller; Sarah Guido |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
First edition |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Sebastopol, CA : |
| Name of publisher, distributor, etc. |
O'Reilly Media, Inc, |
| Date of publication, distribution, etc. |
©2017 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Page number |
xii, 378 pages : |
| Other physical details |
illustrations, |
| Dimensions |
24 cm. |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Title |
Introduction --<br/> |
| -- |
Supervised learning --<br/> |
| -- |
Unsupervised learning and preprocessing --<br/> |
| -- |
Representing data and engineering features --<br/> |
| -- |
Model evaluation and improvement --<br/> |
| -- |
Algorithm chains and pipelines --<br/> |
| -- |
Working with text data --<br/> |
| -- |
Wrapping up. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, you'll learn: Fundamental concepts and applications of machine learning ; Advantages and shortcomings of widely used machine learning algorithms ; How to represent data processed by machine learning, including which data aspects to focus on ; Advanced methods for model evaluation and parameter tuning ; The concept of pipelines for chaining models and encapsulating your workflow ; Methods for working with text data, including text-specific processing techniques ; Suggestions for improving your machine learning and data science skills |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Data mining. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Python (Computer program language) |
| 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 |
Dewey Decimal Classification |
| Koha item type |
Books |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) |
|---|
| -- |
4546 |
