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Apache Spark 2.x Machine Learning Cookbook

You're reading from   Apache Spark 2.x Machine Learning Cookbook Over 100 recipes to simplify machine learning model implementations with Spark

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781783551606
Length 666 pages
Edition 1st Edition
Languages
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Authors (5):
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Broderick Hall Broderick Hall
Author Profile Icon Broderick Hall
Broderick Hall
Meenakshi Rajendran Meenakshi Rajendran
Author Profile Icon Meenakshi Rajendran
Meenakshi Rajendran
Shuen Mei Shuen Mei
Author Profile Icon Shuen Mei
Shuen Mei
Mohammed Guller Mohammed Guller
Author Profile Icon Mohammed Guller
Mohammed Guller
Siamak Amirghodsi Siamak Amirghodsi
Author Profile Icon Siamak Amirghodsi
Siamak Amirghodsi
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Toc

Table of Contents (14) Chapters Close

Preface 1. Practical Machine Learning with Spark Using Scala FREE CHAPTER 2. Just Enough Linear Algebra for Machine Learning with Spark 3. Spark's Three Data Musketeers for Machine Learning - Perfect Together 4. Common Recipes for Implementing a Robust Machine Learning System 5. Practical Machine Learning with Regression and Classification in Spark 2.0 - Part I 6. Practical Machine Learning with Regression and Classification in Spark 2.0 - Part II 7. Recommendation Engine that Scales with Spark 8. Unsupervised Clustering with Apache Spark 2.0 9. Optimization - Going Down the Hill with Gradient Descent 10. Building Machine Learning Systems with Decision Tree and Ensemble Models 11. Curse of High-Dimensionality in Big Data 12. Implementing Text Analytics with Spark 2.0 ML Library 13. Spark Streaming and Machine Learning Library

Curse of High-Dimensionality in Big Data

In this chapter, we will cover the following topics:

  • Two methods of ingesting and preparing a CSV file for processing in Spark
  • Singular Value Decomposition (SVD) to reduce high-dimensionality in Spark
  • Principal Component Analysis (PCA) to pick the most effective latent factor for machine learning in Spark
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