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R Machine Learning Projects

R Machine Learning Projects

By : Dr. Sunil Kumar Chinnamgari
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R Machine Learning Projects

R Machine Learning Projects

1 (1)
By: Dr. Sunil Kumar Chinnamgari

Overview of this book

R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you’ll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You’ll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine. By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations.
Table of Contents (12 chapters)
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10
The Road Ahead

Image Recognition Using Deep Neural Networks

In 1966, Professor Seymour Papert at MIT conceptualized an ambitious summer project titled The Summer Vision Project. The task for the graduate student was to plug a camera into a computer and enable it to understand what it sees! I am sure it would have been super-difficult for the graduate student to have finished this project, as even today the task remains half complete.

A human being, when they look outside, is able to recognize the objects that they see. Without thinking, they are able to classify a cat as a cat, a dog as a dog, a plant as a plant, an animal as an animal—this is happening because the human brain draws knowledge from its extensive prelearned database. After all, as human beings, we have millions of years' worth of evolutionary context that enables us draw inferences from the thing that we see. Computer...

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