Book Image

Advanced Machine Learning with R

By : Cory Lesmeister, Dr. Sunil Kumar Chinnamgari
Book Image

Advanced Machine Learning with R

By: Cory Lesmeister, Dr. Sunil Kumar Chinnamgari

Overview of this book

R is one of the most popular languages when it comes to exploring the mathematical side of machine learning and easily performing computational statistics. This Learning Path shows you how to leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. You’ll work through realistic projects such as building powerful machine learning models with ensembles to predict employee attrition. Next, you’ll explore different clustering techniques to segment customers using wholesale data and even apply TensorFlow and Keras-R for performing advanced computations. Each chapter will help you implement advanced machine learning algorithms using real-world examples. You’ll also be introduced to reinforcement learning along with its use cases and models. Finally, this Learning Path will provide you with a glimpse into how some of these black box models can be diagnosed and understood. By the end of this Learning Path, you’ll be equipped with the skills you need to deploy machine learning techniques in your own projects.
Table of Contents (30 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Chapter 13. Text Mining

"What then is, generally speaking, the truth of history? A fable agreed upon. As it has been very ingeniously remarked"

- Napoleon Bonaparte

The world is awash with textual data. If you Google, Bing, or Yahoo! how much of that data is unstructured, that is, in a textual format, estimates would range from 80 to 90 percent. The real number doesn't matter. It matters that a large proportion of the data is in text format. The implication is that anyone seeking to find insights in that data must develop the capability to process and analyze text.

When I first started out as a market researcher, I used to manually pore through page after page of moderator-led focus group and interview transcripts with the hope of capturing some qualitative insight, an aha moment if you will, and then haggle with fellow team members over whether they had the same insight or not. Then, you would always have that one individual in a project who would swoop in and listen to two interviews—out...