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Mastering Machine Learning with R, Second Edition - Second Edition

You're reading from Mastering Machine Learning with R, Second Edition - Second Edition

Product type Book

Published in Apr 2017

Publisher Packt

ISBN-13 9781787287471

Pages 420 pages

Edition 2nd Edition

Languages

Concepts

Machine Learning

Table of Contents (23) Chapters

Title Page

Credits

About the Author

About the Reviewers

Packt Upsell

Customer Feedback

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 and Deep Learning

8. Cluster Analysis

9. Principal Components Analysis

10. Market Basket Analysis, Recommendation Engines, and Sequential Analysis

11. Creating Ensembles and Multiclass Classification

12. Time Series and Causality

13. Text Mining

14. R on the Cloud

15. R Fundamentals

16. Sources

Summary

In this final chapter, we went through the process of quickly and simply getting R and RStudio running on the cloud. Utilizing AWS in this exercise, we covered step by step how to create a virtual machine (an instance) on the cloud, configure it, launch it and bring up RStudio on a web browser. Finally, we went over how easy it is to load data, by bringing in the climate .csv file from GitHub. With this introduction to cloud computing, you can now perform work anywhere you have an Internet connection, and can quickly scale the power of your instance to meet your needs. That concludes the primary chapters of the book. I hope you've enjoyed it and can implement the methods in here as well as other methods you learn over time. Thank You!

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