- Mastering Machine Learning with R
- Table of Contents
- Mastering Machine Learning with R
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Preface
- 1. A Process for Success
- 2. Linear Regression – The Blocking and Tackling of Machine Learning
- 3. Logistic Regression and Discriminant Analysis
- 4. Advanced Feature Selection in Linear Models
- 5. More Classification Techniques – K-Nearest Neighbors and Support Vector Machines
- 6. Classification and Regression Trees
- 7. Neural Networks
- 8. Cluster Analysis
- 9. Principal Components Analysis
- 10. Market Basket Analysis and Recommendation Engines
- An overview of a market basket analysis
- Business understanding
- Data understanding and preparation
- Modeling and evaluation
- An overview of a recommendation engine
- Business understanding and recommendations
- Data understanding, preparation, and recommendations
- Modeling, evaluation, and recommendations
- Summary
- 11. Time Series and Causality
- 12. Text Mining
- A. R Fundamentals
- Index
As R is a free and open source software, you will only need to download and install it from https://www.r-project.org/. Although it is not mandatory, it is highly recommended that you download IDE and RStudio from https://www.rstudio.com/products/RStudio/.
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