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R Machine Learning Projects

You're reading from   R Machine Learning Projects Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781789807943
Length 334 pages
Edition 1st Edition
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Author (1):
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Dr. Sunil Kumar Chinnamgari Dr. Sunil Kumar Chinnamgari
Author Profile Icon Dr. Sunil Kumar Chinnamgari
Dr. Sunil Kumar Chinnamgari
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Table of Contents (12) Chapters Close

Preface 1. Exploring the Machine Learning Landscape FREE CHAPTER 2. Predicting Employee Attrition Using Ensemble Models 3. Implementing a Jokes Recommendation Engine 4. Sentiment Analysis of Amazon Reviews with NLP 5. Customer Segmentation Using Wholesale Data 6. Image Recognition Using Deep Neural Networks 7. Credit Card Fraud Detection Using Autoencoders 8. Automatic Prose Generation with Recurrent Neural Networks 9. Winning the Casino Slot Machines with Reinforcement Learning 10. The Road Ahead
11. Other Books You May Enjoy

Understanding RL

RL is a very important area but is sometimes overlooked by practitioners for solving complex, real-world problems. It is unfortunate that even most ML textbooks focus only on supervised and unsupervised learning while totally ignorning RL.

RL as an area has picked up momentum in recent years; however, its origins date back to 1980. It was invented by Rich Sutton and Andrew Barto, Rich's PhD thesis advisor. It was thought of as archaic, even back in the 1980s. Rich, however, believed in RL and its promise, maintaining that it would eventually be recognized.

A quick Google search with the term RL shows that RL methods are often used in games, such as checkers and chess. Gaming problems are problems that require taking actions over time to find a long-term optimal solution to a dynamic problem. They are dynamic in the sense that the conditions are constantly...

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