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Python: Real World Machine Learning
book

Python: Real World Machine Learning

by Prateek Joshi, John Hearty, Bastiaan Sjardin, Luca Massaron, Alberto Boschetti
November 2016
Beginner to intermediate
941 pages
21h 55m
English
Packt Publishing
Content preview from Python: Real World Machine Learning

Further reading

A solid place to start understanding Semi-supervised learning methods is Xiaojin Zhu's very thorough literature survey, available at http://pages.cs.wisc.edu/~jerryzhu/pub/ssl_survey.pdf.

I also recommend a tutorial by the same author, available in the slide format at http://pages.cs.wisc.edu/~jerryzhu/pub/sslicml07.pdf.

The key paper on Contastive Pessimistic Likelihood Estimation is Loog's 2015 paper http://arxiv.org/abs/1503.00269.

This chapter made a reference to the distinction between generative and discriminative models. A couple of relatively clear explanations of the distinction between generative and discriminative algorithms are provided by Andrew Ng (http://cs229.stanford.edu/notes/cs229-notes2.pdf) and Michael Jordan ...

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Publisher Resources

ISBN: 9781787123212Supplemental ContentPurchase Link