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

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