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Mastering Predictive Analytics with R, Second Edition

You're reading from   Mastering Predictive Analytics with R, Second Edition Machine learning techniques for advanced models

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781787121393
Length 448 pages
Edition 2nd Edition
Languages
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Authors (2):
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James D. Miller James D. Miller
Author Profile Icon James D. Miller
James D. Miller
Rui Miguel Forte Rui Miguel Forte
Author Profile Icon Rui Miguel Forte
Rui Miguel Forte
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Table of Contents (16) Chapters Close

Preface 1. Gearing Up for Predictive Modeling FREE CHAPTER 2. Tidying Data and Measuring Performance 3. Linear Regression 4. Generalized Linear Models 5. Neural Networks 6. Support Vector Machines 7. Tree-Based Methods 8. Dimensionality Reduction 9. Ensemble Methods 10. Probabilistic Graphical Models 11. Topic Modeling 12. Recommendation Systems 13. Scaling Up 14. Deep Learning Index

What you need for this book

In order to work with and to run the code examples found in this book, the following should be noted:

  • R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows, and MacOS. To download R, there are a variety of locations available, including https://www.rstudio.com/products/rstudio/download.
  • R includes extensive accommodations for accessing documentation and searching for help. A good source of information is at http://www.r-project.org/help.html.
  • The capabilities of R are extended through user-created packages. Various packages are referred to and used throughout this book and the features of and access to each will be detailed as they are introduced. For example, the wordcloud package is introduced in Chapter 11, Topic Modeling to plot a cloud of words shared across documents. This is found at https://cran.r-project.org/web/packages/wordcloud/index.html.
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