[PDF.64br] Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (MIT Press)

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

[PDF.pr88] Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (MIT Press)

Fundamentals of Machine Learning  John D. Kelleher, Brian Mac Namee, Aoife D'Arcy epub
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 | #14793 in Books |  2015-07-24 | Original language:English | PDF # 1 |  9.00 x.88 x7.00l,.0 | File type: PDF | 624 pages

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||10 of 10 people found the following review helpful.| Excellent exposition of practical applied supervised machine learning.|By RiverRider|Supervised machine learning only. Basically a bunch of applications for an undergrad CS class. Light on theory. Very well structured though and excellent if you want to see some applications of machine learning in action. For deeper treatment see coursera courses by Geoff Hinton of Toronto an| ||Erudite yet real-world relevant. It's true that predictive analytics and machine learning go hand-in-hand: To put it loosely, prediction depends on learning from past examples. And, while Fundamentals succeeds as a comprehensive university textbook co

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 a...

You can specify the type of files you want, for your device.Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (MIT Press)   |  John D. Kelleher, Brian Mac Namee, Aoife D'Arcy. I was recommended this book by a dear friend of mine.

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