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Fundamentals of machine learning for predictive data analytics algorithms, worked examples, and case studies

By: Contributor(s): Material type: TextTextLanguage: English Language Publication details: Cambridge, The MIT Press Massachusetts 2015Description: xxii, 595 pages : some Colour 24 cmISBN:
  • 9780262029445
Subject(s): DDC classification:
  • 006.31 KEL
Summary: "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. 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
Item type: Lending Books
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Holdings
Item type Current library Collection Call number Status Notes Date due Barcode Item holds
Lending Books Lending Books Applied Sciences Library Lending Section Lending Collection 006.31KEL (Browse shelf(Opens below)) Available $86.47 112980
Lending Books Lending Books Applied Sciences Library Lending Section Lending Collection 006.31 KEL (Browse shelf(Opens below)) Available $86.47 112981
Total holds: 0

Machine learning for predictive data analytics --
Data to insights to decisions --
Data exploration --
Information-based learning --
Similarity-based learning --
Probability-based learning --
Error-based learning --
Evaluation --
Case study : customer churn --
Case study : galaxy classification --
The art of machine learning for predictive data analytics.

"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. 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

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