Introduction to machine learning with Python : (Record no. 1331)

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
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Source of acquisition Inventory number Total Checkouts Total Renewals Full call number Barcode Checked out Date last seen Date last checked out Cost, replacement price Price effective from Koha item type
    Dewey Decimal Classification     Non-fiction IIITDM Kurnool IIITDM Kurnool General Stacks 08.12.2021 Technical Bureau India Invoice No. TB4232 dated 22.10.2 10 8 005.133 MUL 0004014 22.01.2025 24.12.2024 24.12.2024 1200.00 08.12.2021 Books
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