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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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Philosophy behind ensembling

Ensembling, which is super-famous among ML practitioners, can be well-understood through a simple real-world, non-ML example.

Assume that you have applied for a job in a very reputable corporate organization and you have been called for an interview. It is unlikely you will be selected for a job just based on one interview with an interviewer. In most cases, you will go through multiple rounds of interviews with several interviewers or with a panel of interviewers. The expectation from the organization is that each of the interviewers is an expert on a particular area and that the interviewer has evaluated your fitness for the job based on your experience in the interviewers' area of expertise. Your selection for the job, of course, depends on consolidated feedback from all of the interviewers that talked to you. The organization deems that you...

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