Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies / John D. Kelleher, Brian MacNamee and Aoife D'Arcy.

Author: Kelleher, John D., 1974- author.

Edition Statement:Second edition.

ImprintCambridge, Massachusetts : The MIT Press, [2020]

Descriptionliv, 798 pages : illustrations ; 25 cm

Note: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 -- Deep learning -- Evaluation -- Beyond prediction: unsupervised learning -- Beyond prediction: reinforcement learning -- Case study: customer churn -- Case study: galaxy classification -- The art of machine learning for predictive data analytics -- Descriptive statistics and data visualization for machine learning -- Introduction to probability for machine learning -- Differentiation techniques for machine learning -- Introduction to linear algebra.

Bibliography Note:Includes bibliographical references (pages 775-786) and index.

Note:"A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications."-- Provided by publisher.

Note:Recommended in Resources for College Libraries

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