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Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies by [John D. Kelleher, Brian Mac Namee, Aoife D'Arcy]

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

4.6 out of 5 stars 89 ratings

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Editorial Reviews

Review

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
--This text refers to an out of print or unavailable edition of this title.

About the Author

John D. Kelleher is Academic Leader of the Information, Communication, and Entertainment Research Institute at the Technological University Dublin. He has authored a number of books including: Deep Learning, MIT Press, 2019, Data Science, MIT Press, 2018, and Fundamentals of Machine Learning for Predictive Data Analytics, MIT Press, 2015. --This text refers to an out of print or unavailable edition of this title.

Product details

  • ASIN ‏ : ‎ B08BT9YX8K
  • Publisher ‏ : ‎ The MIT Press (July 31, 2015)
  • Publication date ‏ : ‎ July 31, 2015
  • Language ‏ : ‎ English
  • File size ‏ : ‎ 13609 KB
  • Text-to-Speech ‏ : ‎ Enabled
  • Screen Reader ‏ : ‎ Supported
  • Enhanced typesetting ‏ : ‎ Enabled
  • X-Ray ‏ : ‎ Not Enabled
  • Word Wise ‏ : ‎ Not Enabled
  • Lending ‏ : ‎ Not Enabled
  • Customer Reviews:
    4.6 out of 5 stars 89 ratings

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Customer reviews

4.6 out of 5 stars
4.6 out of 5
89 global ratings

Top reviews from the United States

Reviewed in the United States on March 28, 2018
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14 people found this helpful
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Reviewed in the United States on September 14, 2017
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1.0 out of 5 stars Images too small to read
By Amazon Customer on September 14, 2017
Kindle version: images are too small.
This is particularly bad for special chars and formulas which are rendered as images as they appear about as large as the punctuation.
Normal diagrams are also small and must be viewed with the zoom function.

Apologies for rating the book based on formatting, but there's no other apparent way to contact the publisher.
Once the issues are resolved I will fix the rating to fix the "outlier" it has created.
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41 people found this helpful
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Reviewed in the United States on March 12, 2017
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33 people found this helpful
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Reviewed in the United States on June 14, 2018
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6 people found this helpful
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Reviewed in the United States on January 31, 2020
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Reviewed in the United States on June 10, 2018
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Reviewed in the United States on August 1, 2019
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Reviewed in the United States on September 27, 2018
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Top reviews from other countries

John Grant
5.0 out of 5 stars Five Stars
Reviewed in the United Kingdom on August 17, 2016
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2 people found this helpful
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ABCD
5.0 out of 5 stars Amazing, informative, and comprehensive
Reviewed in the United Kingdom on October 21, 2020
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5.0 out of 5 stars Amazing, informative, and comprehensive
Reviewed in the United Kingdom on October 21, 2020
Well packed. One of the most amazing, informative, and comprehensive machine learning books. Presents information in a great way and well structured with great case studies
Highly recommended!
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Mukesh Rao
5.0 out of 5 stars One of the best books on Data Science
Reviewed in India on October 24, 2016
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4 people found this helpful
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gffyh
5.0 out of 5 stars Introduccion a ML
Reviewed in Mexico on January 16, 2019
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Amazon Customer
4.0 out of 5 stars Well written book , Missing some Techniques
Reviewed in Australia on June 25, 2020
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