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Practical Machine Learning

You're reading from   Practical Machine Learning Learn how to build Machine Learning applications to solve real-world data analysis challenges with this Machine Learning book – packed with practical tutorials

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Product type Paperback
Published in Jan 2016
Publisher Packt
ISBN-13 9781784399689
Length 468 pages
Edition 1st Edition
Languages
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Author (1):
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Sunila Gollapudi Sunila Gollapudi
Author Profile Icon Sunila Gollapudi
Sunila Gollapudi
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Table of Contents (16) Chapters Close

Preface 1. Introduction to Machine learning FREE CHAPTER 2. Machine learning and Large-scale datasets 3. An Introduction to Hadoop's Architecture and Ecosystem 4. Machine Learning Tools, Libraries, and Frameworks 5. Decision Tree based learning 6. Instance and Kernel Methods Based Learning 7. Association Rules based learning 8. Clustering based learning 9. Bayesian learning 10. Regression based learning 11. Deep learning 12. Reinforcement learning 13. Ensemble learning 14. New generation data architectures for Machine learning Index

Reinforcement Learning (RL)

Let's do a recap of supervised, semi-supervised, and unsupervised learning, and set the context for Reinforcement Learning. In Chapter 1, Introduction to Machine Learning, we covered the basic definitions of supervised, semi-supervised, and unsupervised learning. Inductive learning is a reasoning process that uses the results of one experiment to run the next set of experiments and iteratively evolve a model from specific information.

The following figure depicts various subfields of Machine learning. These subfields are one of the ways the Machine learning algorithms are classified:

Reinforcement Learning (RL)

Supervised learning is all about operating to a known expectation, and in this case, what needs to be analyzed from the data being defined. The input datasets in this context are also referred to as labeled datasets. Algorithms classified under this category focus on establishing a relationship between the input and output attributes and uses this relationship speculatively to...

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