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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

This chapter was about how to set up you and your team for success in any project that you tackle. The CRISP-DM process is put forward as a flexible and comprehensive framework in order to facilitate the softer skills of communication and influence. Each step of the process and the tasks in each step were enumerated. More than that, the commentary provides some techniques and considerations to with the process execution. By taking heed of the process, you can indeed become an agent of positive change to any organization.

The other item put forth in this chapter was an algorithm flowchart; a cheat sheet to help identify some of the proper techniques to apply in order to solve the business problem. With this foundation in place, we can now move on to applying these techniques to real-world problems.

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Mastering Machine Learning with R, Second Edition - Second Edition

Published in: Apr 2017 Publisher: Packt ISBN-13: 9781787287471

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