Search icon
Subscription
0
Cart icon
Close icon
You have no products in your basket yet
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletters
Free Learning
Arrow right icon
Essential PySpark for Scalable Data Analytics

You're reading from  Essential PySpark for Scalable Data Analytics

Product type Book
Published in Oct 2021
Publisher Packt
ISBN-13 9781800568877
Pages 322 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Sreeram Nudurupati Sreeram Nudurupati
Profile icon Sreeram Nudurupati

Table of Contents (19) Chapters

Preface 1. Section 1: Data Engineering
2. Chapter 1: Distributed Computing Primer 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...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $15.99/month. Cancel anytime}