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Mastering Machine Learning with R, Second Edition

You're reading from   Mastering Machine Learning with R, Second Edition Advanced prediction, algorithms, and learning methods with R 3.x

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781787287471
Length 420 pages
Edition 2nd Edition
Languages
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Author (1):
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Cory Lesmeister Cory Lesmeister
Author Profile Icon Cory Lesmeister
Cory Lesmeister
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Table of Contents (17) Chapters Close

Preface 1. A Process for Success 2. Linear Regression - The Blocking and Tackling of Machine Learning FREE CHAPTER 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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