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Machine Learning with R - Third Edition

You're reading from  Machine Learning with R - Third Edition

Product type Book
Published in Apr 2019
Publisher Packt
ISBN-13 9781788295864
Pages 458 pages
Edition 3rd Edition
Languages
Author (1):
Brett Lantz Brett Lantz
Profile icon Brett Lantz

Table of Contents (18) Chapters

Machine Learning with R - Third Edition
Contributors
Preface
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Introducing Machine Learning Managing and Understanding Data Lazy Learning – Classification Using Nearest Neighbors Probabilistic Learning – Classification Using Naive Bayes Divide and Conquer – Classification Using Decision Trees and Rules Forecasting Numeric Data – Regression Methods Black Box Methods – Neural Networks and Support Vector Machines Finding Patterns – Market Basket Analysis Using Association Rules Finding Groups of Data – Clustering with k-means Evaluating Model Performance Improving Model Performance Specialized Machine Learning Topics Index

Summary


In this chapter, we examined two machine learning methods that offer a great deal of potential but are often overlooked due to their complexity. Hopefully, you now see that this reputation is at least somewhat undeserved. The basic concepts that drive ANNs and SVMs are fairly easy to understand.

On the other hand, because ANNs and SVMs have been around for many decades, each of them has numerous variations. This chapter just scratches the surface of what is possible with these methods. By utilizing the terminology that you learned here, you should be capable of picking up the nuances that distinguish the many advancements that are being developed every day, including the ever-growing field of deep learning.

Now that we have spent some time learning about many different types of predictive models, from simple to sophisticated, in the next chapter, we will begin to consider methods for other types of learning tasks. These unsupervised learning techniques will bring to light fascinating...

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