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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

Sequential data analysis

There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don't know. But there are also unknown unknowns. There are things we don't know we don't know. - Donald Rumsfeld, Former Secretary of Defense

The very first business question I came across after the 1st edition was published revolved around product sequential analysis. The team worked on complicated Excel spreadsheets and pivot tables, along with a bunch of SAS code, to produce insights. After coming across this problem, I explored what could be done with R and was pleasantly surprised to stumble into the TraMineR package, specifically designed for just such a task. I believe the application of R to the problem would have greatly simplified the analysis.

The package was designed for the social sciences, but it can be used in just about every situation where you want to mine...

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