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Applied Unsupervised Learning with R

You're reading from Applied Unsupervised Learning with R Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA

Product type Paperback

Published in Mar 2019

Publisher

ISBN-13 9781789956399

Length 320 pages

Edition 1st Edition

Languages

R

Concepts

Machine Learning

Authors (2):

Bradford Tuckfield

Alok Malik

View More author details

Table of Contents (9) Chapters

Applied Unsupervised Learning with R

Preface

1. Introduction to Clustering Methods FREE CHAPTER

2. Advanced Clustering Methods

3. Probability Distributions

4. Dimension Reduction

5. Data Comparison Methods

6. Anomaly Detection

Appendix

Summary

Congratulations on completing the third chapter of the book! In this chapter, we learned the types of standard probability distribution, as well as when and how to generate them in R. We also learned how to find PDFs and CDFs of unknown distributions with KDE. In the final section, we learned how to compare two samples and determine whether they belong to the same distribution in R. In further chapters, we will learn about other types of unsupervised learning techniques that will help not only in exploratory data analysis but also give us other useful insights into data as well.

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Authors (2)

Alok Malik

Alok Malik

Alok Malik is a data scientist based in India. He has previously worked on creating and deploying unsupervised learning solutions in fields such as finance, cryptocurrency trading, logistics, and natural language processing. He has a bachelor's degree in technology from the Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, where he studied electronics and communication engineering.

See other products by Alok Malik

Bradford Tuckfield

Bradford Tuckfield

Bradford Tuckfield has designed and implemented data science solutions for firms in a variety of industries. He studied math for his bachelor's degree and economics for his Ph.D. He has written for scholarly journals and the popular press, on topics including linear algebra, psychology, and public policy.

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