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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 computer vision

In today's world, we have advanced cameras that are very successful at mimicking how a human eye captures light and color; but image-capturing in the right way is just stage one in the whole image-comprehension aspect. Post image-capturing, we will need to enable technology that interprets what has been captured and build context around it. This is what the human brain does when the eyes see something. Here comes the huge challenge: we all know that computers see images as huge piles of integer values that represent intensities across a spectrum of colors, and of course, computer have no context associated with the image itself. This is where ML comes into play. ML allows us to train a context for a dataset such that it enables computers to understand what objects certain sequences of numbers actually represent.

Computer vision is one of the...

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