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

Understanding language models

In the English language, the character a appears much more often in words and sentences than the character x. Similarly, we can also observe that the word is occurs more frequently than the word specimen. It is possible to learn the probability distributions of characters and words by examining large volumes of text. The following screenshot is a chart showing the probability distribution of letters given a corpus (text dataset):

Probability distribution of letters in a corpus

We can observe that the probability distributions of characters are non-uniform. This essentially means that we can recover the characters in a word, even if they are lost due to noise. If a particular character is missing in a word, it can be reconstructed just based on the characters that are surrounding the missing character. The reconstruction of the missing character is...