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

Building a text sentiment classifier with GloVe word embedding

Stanford University's Pennington, et al. developed an extension of the word2vec method that is called Global Vectors for Word Representation (GloVe) for efficiently learning word vectors.

GloVe combines the global statistics of matrix factorization techniques, such as LSA, with the local context-based learning in word2vec. Also, unlike word2vec, rather than using a window to define local context, GloVe constructs an explicit word context or word co-occurrence matrix using statistics across the whole text corpus. As an effect, the learning model yields generally better word embeddings.

The text2vec library in R has a GloVe implementation that we could use to train to obtain word embeddings from our own training corpus. Alternatively, pretrained GloVe word embeddings can be downloaded and reused, similar to the...