Python machine learning by example: (Record no. 3654)

MARC details
000 -LEADER
fixed length control field 01974nam a22002057a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20221027112838.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 221027b ||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781789616729
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.133
Item number LIU
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Liu, Yuxi
245 ## - TITLE STATEMENT
Title Python machine learning by example:
Remainder of title implement machine learning algorithms and techniques to build intelligent systems
250 ## - EDITION STATEMENT
Edition statement 2nd
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc. Packt Publishing
Place of publication, distribution, etc. Birmingham
Date of publication, distribution, etc. 2019
300 ## - PHYSICAL DESCRIPTION
Extent vi, 355 p.
365 ## - TRADE PRICE
Price type code USD
Price amount 36.99
520 ## - SUMMARY, ETC.
Summary, etc. About this book<br/>The surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions. If you’re interested in ML, this book will serve as your entry point to ML.<br/><br/>Python Machine Learning By Example begins with an introduction to important ML concepts and implementations using Python libraries. Each chapter of the book walks you through an industry adopted application. You’ll implement ML techniques in areas such as exploratory data analysis, feature engineering, and natural language processing (NLP) in a clear and easy-to-follow way.<br/><br/>With the help of this extended and updated edition, you’ll understand how to tackle data-driven problems and implement your solutions with the powerful yet simple Python language and popular Python packages and tools such as TensorFlow, scikit-learn, gensim, and Keras. To aid your understanding of popular ML algorithms, the book covers interesting and easy-to-follow examples such as news topic modeling and classification, spam email detection, stock price forecasting, and more.<br/><br/>By the end of the book, you’ll have put together a broad picture of the ML ecosystem and will be well-versed with the best practices of applying ML techniques to make the most out of new opportunities.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Python (Computer program language)
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Book
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Bill No Bill Date Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Total Renewals Full call number Accession Number Date last seen Date checked out Copy number Cost, replacement price Price effective from Koha item type
    Dewey Decimal Classification     IT & Decisions Sciences 369/22-23 12-10-2022 Indian Institute of Management LRC Indian Institute of Management LRC General Stacks 10/27/2022 T V Enterprises 2011.34 2 2 005.133 LIU 003427 08/07/2023 07/08/2023 1 3059.07 10/27/2022 Book

©2019-2020 Learning Resource Centre, Indian Institute of Management Bodhgaya

Powered by Koha