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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

Feature extraction

A machine learning model is equivalent to a function in mathematics or a method in computer programming. A machine learning model takes one or more parameters or variables as input and yields an output, called a prediction. In machine learning terminology, these input parameters or variables are called features. A feature is a column of the input dataset within a machine learning algorithm or model. A feature is a measurable data point, such as an individual's name, gender, or age, or it can be time-related data, weather, or some other piece of data that is useful for analysis.

Machine learning algorithms leverage linear algebra, a field of mathematics, and make use of mathematical structures such as matrices and vectors to represent data internally and also within the code level implementation of algorithms. Real-world data, even after undergoing the data engineering process, rarely occurs in the form of matrices and vectors. Therefore, the feature engineering...

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