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019 a| 1198713711;

020 a| 9780262361101q| (electronic book);

020 a| 0262361108q| (electronic book);

020 a| 9780262364911q| (electronic book);

020 a| 0262364913q| (electronic book);

020 z| 9780262044691q| (hardcover);

020 z| 0262044692q| (hardcover);

028 02 a| EB00811293b| Recorded Books;

035 a| (Sirsi) o1162184998;

035 a| (OCoLC)1162184998z| (OCoLC)1198713711;

049 a| EREE;

050 4 a| Q325.5b| .K455 2020eb;

082 04 a| 006.3/12| 23/eng/20230323;

100 1 a| Kelleher, John D.,d| 1974-e| author.1| https://id.oclc.org/worldcat/entity/E39PBJkTbwcmCvMvcxQJmydxXd=| ^A1283950;

245 10 a| Fundamentals of machine learning for predictive data analytics :b| algorithms, worked examples, and case studies /c| John D. Kelleher, Brian Mac Namee, Aoife D'Arcy.;

246 30 a| Algorithms, worked examples, and case studies;

250 a| Second edition.;

264 1 a| Cambridge, Massachusetts :b| MIT Press,c| [2020];

300 a| 1 online resource (liv, 798 pages) :b| illustrations.;

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

505 00 g| I.t| Introduction to machine learning and data analytics:g| 1.t| Machine learning for predictive data analysis --g| 2.t| Data to insights to decisions --g| 3.t| Data exploration --g| II.t| Predictive data analytics:g| 4.t| Information-based learning --g| 5. Similarity-based learning --g| 6.t| Probaility-based learning --g| 7.t| Error-based learning --g| 8.t| Deep learning --g| 9.t| Evaluation --g| III.t| Beyond prediction:g| 10.t| Beyond prediction: unsupervised learning --g| 11.t| Beyond prediction: reinforcement learning --g| IV.t| Case studies and conclusions:g| 12.t| Case study: customer churn --g| 13.t| Case study: galaxy classification --g| 14.t| The art of machine learning for predictive data analytics --g| V.t| Appendices:g| A.t| Descriptive statistics and data visualization for machine learning --g| B.t| Introduction to probability for machine learning --g| C.t| Differentiation techniques --g| D.t| Introduction to linear algebra.;

500 a| Available through EBookCentral.;

504 a| Includes bibliographical references and index.;

520 a| A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.;

588 0 a| Description based on resource viewed on March 23, 2023.;

650 0 a| Machine learning.=| ^A145211;

650 0 a| Data mining.=| ^A408868;

650 0 a| Prediction theory.=| ^A6714;

650 2 a| Data Mining.0| (DNLM)D057225=| ^A1023265;

650 2 a| Machine Learning.0| (DNLM)D000069550=| ^A1345552;

650 7 a| COMPUTERSx| Artificial Intelligencex| General.2| bisacsh;

650 7 a| Data mining.2| fast0| (OCoLC)fst00887946;

650 7 a| Machine learning.2| fast0| (OCoLC)fst01004795;

650 7 a| Prediction theory.2| fast0| (OCoLC)fst01075037;

700 1 a| Mac Namee, Brian,e| author.=| ^A1473446;

700 1 a| D'Arcy, Aoife,d| 1978-e| author.1| https://id.oclc.org/worldcat/entity/E39PCjxMytRFtfcDBxJXmJMqXq=| ^A1283951;

710 2 a| ProQuest (Firm)=| ^A1048343;

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938 a| Askews and Holts Library Servicesb| ASKHn| AH37730929;

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Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies / John D. Kelleher, Brian Mac Namee, Aoife D'Arcy.

Author/creator	Kelleher, John D., 1974- author.
Other author	Mac Namee, Brian, author.
Other author	D'Arcy, Aoife, 1978- author.
Other author	ProQuest (Firm)
Format	Electronic
Edition	Second edition.
Publication	Cambridge, Massachusetts : MIT Press, [2020]
Copyright Date	©2020
Description	1 online resource (liv, 798 pages) : illustrations.
Supplemental Content	EBSCOhost
Subjects	Machine learning. Data mining. Prediction theory. Data Mining. Machine Learning.

Portion of title	Algorithms, worked examples, and case studies
Contents	I. Introduction to machine learning and data analytics: 1. Machine learning for predictive data analysis -- 2. Data to insights to decisions -- 3. Data exploration -- II. Predictive data analytics: 4. Information-based learning -- 5. Similarity-based learning -- 6. Probaility-based learning -- 7. Error-based learning -- 8. Deep learning -- 9. Evaluation -- III. Beyond prediction: 10. Beyond prediction: unsupervised learning -- 11. Beyond prediction: reinforcement learning -- IV. Case studies and conclusions: 12. Case study: customer churn -- 13. Case study: galaxy classification -- 14. The art of machine learning for predictive data analytics -- V. Appendices: A. Descriptive statistics and data visualization for machine learning -- B. Introduction to probability for machine learning -- C. Differentiation techniques -- D. Introduction to linear algebra.
Abstract	A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
General note	Available through EBookCentral.
Bibliography note	Includes bibliographical references and index.
Source of description	Description based on resource viewed on March 23, 2023.
Issued in other form	Print version: Kelleher, John D., 1974- Fundamentals of machine learning for predictive data analytics. Second edition. Cambridge, Massachusetts : The MIT Press, [2020] 9780262044691
LCCN	2020002998
ISBN	9780262361101 (electronic book)
ISBN	0262361108 (electronic book)
ISBN	9780262364911 (electronic book)
ISBN	0262364913 (electronic book)
ISBN	(hardcover)
ISBN	(hardcover)
Publisher number	EB00811293 Recorded Books

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Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies / John D. Kelleher, Brian Mac Namee, Aoife D'Arcy.

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