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Fundamentals of machine learning for predictive data analytics: (Record no. 3846)

MARC details
000 -LEADER
fixed length control field	02408nam a22002177a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field	20221122111447.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field	221122b \|\|\|\|\| \|\|\|\| 00\| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number	9780262044691
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number	006.31
Item number	KEL
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name	Kelleher, John D.
245 ## - TITLE STATEMENT
Title	Fundamentals of machine learning for predictive data analytics:
Remainder of title	algorithms, worked examples, and case studies
250 ## - EDITION STATEMENT
Edition statement	2nd
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc.	The MIT press
Place of publication, distribution, etc.	Cambridge
Date of publication, distribution, etc.	2020
300 ## - PHYSICAL DESCRIPTION
Extent	liv, 798 p.
365 ## - TRADE PRICE
Price type code	USD
Price amount	80.00
520 ## - SUMMARY, ETC.
Summary, etc.	The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice.<br/><br/>Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.<br/><br/>The book is accessible, offering nontechnical explanations of the ideas underpinning each approach before introducing mathematical models and algorithms. It is focused and deep, providing students with detailed knowledge on core concepts, giving them a solid basis for exploring the field on their own. Both early chapters and later case studies illustrate how the process of learning predictive models fits into the broader business context. The two case studies describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book can be used as a textbook at the introductory level or as a reference for professionals.
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	Prediction theory
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element	Data mining
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	Full call number	Accession Number	Date last seen	Copy number	Cost, replacement price	Price effective from	Koha item type
		Dewey Decimal Classification			IT & Decisions Sciences	TB1974	28-10-2022	Indian Institute of Management LRC	Indian Institute of Management LRC	General Stacks	11/22/2022	Technical Bureau India Pvt. Ltd.	4350.02		006.31 KEL	003700	11/22/2022	1	6616.00	11/22/2022	Book