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020			_a9780262029445
040			_aIIITD
082			_a006.31 _bKEL-F
100			_aKelleher, John D.
245			_aFundamentals of machine learning for predictive data analytics : _balgorithms, worked examples, and case studies _cby John D. Kelleher, Brian Mac Namee and Aoife D'Arcy
260			_aCambridge : _bMIT Press, _c©2015
300			_axxii, 595 p. : _bill. ; _c25 cm.
504			_aIncludes bibliographical references and index.
505			_t1. Machine learning for predictive data analytics
505			_t2. Data to insights to decisions
505			_t3. Data exploration
505			_t4. Information-based learning
505			_t5. Similarity-based learning
505			_t6. Probability-based learning
505			_t7. Error-based learning
505			_t8. Evaluation
505			_t9. Case study : customer churn
505			_t10. Case study : galaxy classification
505			_t11. The art of machine learning for predictive data analytics
520			_aMachine 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. After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals
650			_aMachine learning
650			_aComputers -- Artificial Intelligence
650			_aData mining
650			_aPrediction theory
700			_aNamee, Brian Mac
700			_aD'Arcy, Aoife
942			_2ddc _cBK
999			_c209231 _d209231