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Practical Machine Learning with R

You're reading from   Practical Machine Learning with R Define, build, and evaluate machine learning models for real-world applications

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Product type Paperback
Published in Aug 2019
Publisher Packt
ISBN-13 9781838550134
Length 416 pages
Edition 1st Edition
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Authors (3):
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Brindha Priyadarshini Jeyaraman Brindha Priyadarshini Jeyaraman
Author Profile Icon Brindha Priyadarshini Jeyaraman
Brindha Priyadarshini Jeyaraman
Ludvig Renbo Olsen Ludvig Renbo Olsen
Author Profile Icon Ludvig Renbo Olsen
Ludvig Renbo Olsen
Monicah Wambugu Monicah Wambugu
Author Profile Icon Monicah Wambugu
Monicah Wambugu
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Toc

Table of Contents (8) Chapters Close

About the Book 1. An Introduction to Machine Learning 2. Data Cleaning and Pre-processing FREE CHAPTER 3. Feature Engineering 4. Introduction to neuralnet and Evaluation Methods 5. Linear and Logistic Regression Models 6. Unsupervised Learning 1. Appendix

Unsupervised Learning

Learning Objectives

By the end of this chapter, you will be able to:

  • Distinguish between unsupervised and supervised learning
  • Implement different techniques applied in clustering, such as soft and hard clustering, monothetic and polythetic clustering, and bottom-up versus top-down clustering
  • Perform k-means clustering
  • Compare performance using DIANA, AGNES, and k-means

In this chapter, we aim to equip you with a practical understanding of unsupervised learning.

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