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Essential PySpark for Scalable Data Analytics

You're reading from   Essential PySpark for Scalable Data Analytics A beginner's guide to harnessing the power and ease of PySpark 3

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Product type Paperback
Published in Oct 2021
Publisher Packt
ISBN-13 9781800568877
Length 322 pages
Edition 1st Edition
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Author (1):
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Sreeram Nudurupati Sreeram Nudurupati
Author Profile Icon Sreeram Nudurupati
Sreeram Nudurupati
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Data Engineering
2. Chapter 1: Distributed Computing Primer FREE CHAPTER 3. Chapter 2: Data Ingestion 4. Chapter 3: Data Cleansing and Integration 5. Chapter 4: Real-Time Data Analytics 6. Section 2: Data Science
7. Chapter 5: Scalable Machine Learning with PySpark 8. Chapter 6: Feature Engineering – Extraction, Transformation, and Selection 9. Chapter 7: Supervised Machine Learning 10. Chapter 8: Unsupervised Machine Learning 11. Chapter 9: Machine Learning Life Cycle Management 12. Chapter 10: Scaling Out Single-Node Machine Learning Using PySpark 13. Section 3: Data Analysis
14. Chapter 11: Data Visualization with PySpark 15. Chapter 12: Spark SQL Primer 16. Chapter 13: Integrating External Tools with Spark SQL 17. Chapter 14: The Data Lakehouse 18. Other Books You May Enjoy

Chapter 8: Unsupervised Machine Learning

In the previous two chapters, you were introduced to the supervised learning class of machine learning algorithms, their real-world applications, and how to implement them at scale using Spark MLlib. In this chapter, you will be introduced to the unsupervised learning category of machine learning, where you will learn about parametric and non-parametric unsupervised algorithms. A few real-world applications of clustering and association algorithms will be presented to help you understand the applications of unsupervised learning to solve real-life problems. You will gain basic knowledge and understanding of clustering and association problems when using unsupervised machine learning. We will also look at the implementation details of a few clustering algorithms in Spark ML, such as K-means clustering, hierarchical clustering, latent Dirichlet allocation, and an association algorithm called alternating least squares.

In this chapter, we&apos...

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