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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 FREE CHAPTER 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 chapter, the goal was to get you up and running in the exciting world of neural networks and deep learning. We examined how the methods work, their benefits, and their inherent drawbacks with applications to two different datasets. These techniques work well where complex, nonlinear relationships exist in the data. However, they are highly complex, potentially require a ton of hyper-parameter tuning, are the quintessential black boxes, and are difficult to interpret. We don't know why the self-driving car made a right on red, we just know that it did so properly. I hope you will apply these methods by themselves or supplement other methods in an ensemble modeling fashion. Good luck and good hunting! We will now shift gears to unsupervised learning, starting with clustering.

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