In this chapter, we looked at how to address the massive volume of textual data that exists through text mining methods. We looked at a useful framework for text mining, including preparation, word frequency counts and visualization, and topic models using LDA with the tm package. Included in this framework were other quantitative techniques, such as polarity and formality, in order to provide a deeper lexical understanding, or what one could call style, with the qdap package. The framework was then applied to president Obama's seven State of the Union addresses, which showed that, although the speeches had a similar style, the core messages changed over time as the political landscape changed. Despite it not being practical to cover every possible text mining technique, those discussed in this chapter should be adequate for most problems that one might face. In the next chapter, we are going to shift gears away from building models and focus on a technique to get R on the cloud...
Mastering Machine Learning with R, Second Edition - Second Edition
Mastering Machine Learning with R, Second Edition - Second Edition
Overview of this book
This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more.
You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you’ll understand how these concepts work and what they do.
With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets.
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Current Title:
Mastering Machine Learning with R, Second Edition - Second Edition
Table of Contents (23 chapters)
Title Page
Credits
About the Author
About the Reviewers
Packt Upsell
Customer Feedback
Preface
Free Chapter
A Process for Success
Linear Regression - The Blocking and Tackling of Machine Learning
Logistic Regression and Discriminant Analysis
Advanced Feature Selection in Linear Models
More Classification Techniques - K-Nearest Neighbors and Support Vector Machines
Classification and Regression Trees
Neural Networks and Deep Learning
Cluster Analysis
Principal Components Analysis
Market Basket Analysis, Recommendation Engines, and Sequential Analysis
Creating Ensembles and Multiclass Classification
Time Series and Causality
Text Mining
R on the Cloud
R Fundamentals
Sources
Customer Reviews