Fundamentals Of Machine Learning For Predictive Data Analytics: Algorithms, Worked Examples, And Case Studies by John D. KelleherFundamentals Of Machine Learning For Predictive Data Analytics: Algorithms, Worked Examples, And Case Studies by John D. Kelleher

Fundamentals Of Machine Learning For Predictive Data Analytics: Algorithms, Worked Examples, And…

byJohn D. Kelleher, Brian Mac Namee, Aoife D'arcy

Hardcover | July 24, 2015

Pricing and Purchase Info

$104.00

Earn 520 plum® points

Prices and offers may vary in store

HURRY! ONLY 1 LEFT!
Quantity:

In stock online

Ships free on orders over $25

Not available in stores

about

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

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.

John D. Kelleher is a Lecturer at the Dublin Institute of Technology, and a founding member of DIT's Applied Intelligence Research Center. Brian Mac Namee is a Lecturer at University College Dublin. Aoife D'Arcy is CEO of The Analytics Store, a data analytics consultancy and training company.
Loading
Title:Fundamentals Of Machine Learning For Predictive Data Analytics: Algorithms, Worked Examples, And…Format:HardcoverProduct dimensions:624 pages, 9 × 7 × 1.12 inShipping dimensions:9 × 7 × 1.12 inPublished:July 24, 2015Publisher:The MIT PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0262029448

ISBN - 13:9780262029445

Reviews

Editorial Reviews

This is a wonderful self-contained book that touches upon the essential aspects of machine learning and presents them in a clear and intuitive light. With its incremental discussions ranging from anecdotal accounts underlying the 'big idea' to more complex information theoretic, probabilistic, statistic, and optimization theoretic concepts, its emphasis on how to turn a business problem into an analytics solution, and its pertinent case studies and illustrations, this book makes for an easy and compelling read, which I recommend greatly to anyone interested in finding out more about machine learning and its applications to predictive analytics.-Nathalie Japkowicz, Professor of Computer Science, University of Ottawa; coauthor of Evaluating Learning Algorithms: A Classification Perspective