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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
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Fundamental aspects of recommendation engines

While the basic intent of showing recommendations is to push sales, they actually serve just beyond the better sales concept. Highly personalized content is something recommendation engines are able to deliver. This essentially means that recommendation engines on a retail platform such as Amazon are able to offer the right content to the right customer at the right time through the right channel. It makes sense to provide personalized content; after all, there is no point in showing an irrelevant product to a customer. Also, with the lower attention spans of customers, businesses want to be able to maximize their selling opportunities by showing the right products and encouraging them to buy the right products. At a very high level, personalized content recommendation is achieved in AI in several ways:

  • Mapping similar products that...
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