Abbey's Bookshop Logo
Go to my checkout basket
Login to Abbey's Bookshop
Register with Abbey's Bookshop
Gift Vouchers
Browse by Category

facebook
Google Book Preview
Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case...
— —
John D. Kelleher (Dublin Institute of Technology) Brian Mac Namee (Dublin Institute of Technology)
Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies by John D. Kelleher (Dublin Institute of Technology) at Abbey's Bookshop,

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

John D. Kelleher (Dublin Institute of Technology) Brian Mac Namee (Dublin Institute of Technology) Aoife D'Arcy


9780262029445

MIT Press


Machine learning


Hardback

624 pages

$156.00
We can order this in for you
How long will it take?
order qty:  
Add this item to my basket

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.

By:   John D. Kelleher (Dublin Institute of Technology), Brian Mac Namee (Dublin Institute of Technology), Aoife D'Arcy
Imprint:   MIT Press
Country of Publication:   United States
Dimensions:   Height: 229mm,  Width: 178mm,  Spine: 29mm
Weight:   1.021kg
ISBN:   9780262029445
ISBN 10:   0262029448
Series:   The MIT Press
Pages:   624
Publication Date:   July 2015
Recommended Age:   From 18
Audience:   College/higher education ,  Further / Higher Education
Format:   Hardback
Publisher's Status:   Unspecified

John D. Kelleher is Academic Leader of the Information, Communication, and Entertainment Research Institute at the Technological University Dublin. He is the coauthor of Data Science (also in the MIT Press Essential Knowledge series) and Fundamentals of Machine Learning for Predictive Data Analytics (MIT Press). 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.

My Shopping Basket
Your cart does not contain any items.