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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

2. Advanced Clustering Methods FREE CHAPTER

3. Probability Distributions

4. Dimension Reduction

5. Data Comparison Methods

6. Anomaly Detection

Appendix

Introduction

In this chapter, we're going to study another aspect of unsupervised learning, called probability distributions. Probability distributions are part of classical statistics covered in many mathematical textbooks and courses. With the advent of big data, we've started using probability distributions in exploratory data analysis and other modeling applications as well. So, in this chapter, we're going to study how to use probability distributions in unsupervised learning.

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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.

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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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