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Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (MIT Press)

Amazon.com Price:  $50.59 (as of 14/04/2019 21:27 PST- Details)

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Description

A comprehensive introduction to crucial machine learning approaches utilized in predictive data analytics, covering both theoretical concepts and practical applications.

Machine learning is ceaselessly used to build predictive models by extracting patterns from large datasets. These models are utilized in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused remedy of crucial machine learning approaches utilized in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical subject matter is augmented with explanatory worked examples, and case studies illustrate the applying of these models within 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 and every of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. After all, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through Each and every 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 to be used 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.

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