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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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Toc

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

Datasets

Each chapter of the book describes an ML project that solves a business problem using an ML algorithm or a set of algorithms that we attempt to learn in that specific chapter. The projects considered are from different domains ranging from health care, to banking and finance, and to robots. The business problems solved in the chapters that follow are carefully selected to demonstrate solving a close-to-real-world business use case. The datasets used for the problems are popular open datasets. This will help us not only to explore the solutions covered in this book but also to examine other solutions that are developed for the problem. The problem solved in each of the chapters enriches our experience by applying ML algorithms in various domains and helps us get an understanding of how to solve the business problems in various domains successfully.

You have been reading a chapter from
R Machine Learning Projects
Published in: Jan 2019
Publisher: Packt
ISBN-13: 9781789807943
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