Book Image

R Machine Learning Projects

By : Dr. Sunil Kumar Chinnamgari
Book Image

R Machine Learning Projects

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)
The Road Ahead


In this chapter, we learned about computer vision and its association with deep learning. We explored a specific type of deep learning algorithm, CNNs, that is widely used in computer vision. We studied an open source deep learning framework called MXNet. After a detailed discussion of the MNIST dataset, we built models using various network architectures and successfully classified the handwritten digits in the MNIST dataset. At the end of the chapter, we delved into the concept of transfer learning and explored its association with computer vision. The last project we built in this chapter classified images using an Inception-BatchNorm pretrained model.

In the next chapter, we will explore an unsupervised learning algorithm called the autoencoder neural network. I am really excited to implement a project to capture credit card fraud using autoencoders. Are you game...