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 word embedding

The BoW models that we discussed in our earlier section suffer from a problem that they do not capture information about a word’s meaning or context. This means that potential relationships, such as contextual closeness, are not captured across collections of words. For example, the approach cannot capture simple relationships, such as determining that the words "cars" and "buses" both refer to vehicles that are often discussed in the context of transportation. This problem that we experience with the BoW approach will be overcome by word embedding, which is an improved approach to mapping semantically similar words.

Word vectors represent words as multidimensional continuous floating point numbers, where semantically similar words are mapped to proximate points in geometric space. For example, the words fruit and leaves...