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Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies by D'Arcy, Aoife;Kelleher, John D.;Mac Namee, Brian is a nonfiction available to read on EtoBox.

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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 D

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

Author

D'Arcy, Aoife;Kelleher, John D.;Mac Namee, Brian

Publisher

The MIT Press

Published

2015

Language

EN

ISBN

9780262331746

Category

nonfiction

Subjects

Science, Computer Science, Stem

Rating

5 / 5 (1 ratings)

Updated

2026-03-24