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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

Summary

In this chapter, you learned about the concept of ML and the different types of ML algorithms. You also learned about some of the real-world applications of ML to help businesses minimize losses and maximize revenues and accelerate their time to market. You were introduced to the necessity of scalable ML and two different techniques for scaling out ML algorithms. Apache Spark's native ML Library, MLlib, was introduced, along with its major components.

Finally, you learned a few techniques to perform data wrangling to clean, manipulate, and transform data to make it more suitable for the data science process. In the following chapter, you will learn about the send phase of the ML process, called feature extraction and feature engineering, where you will learn to apply various scalable algorithms to transform individual data fields to make them even more suitable for data science applications.

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