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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
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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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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

Latent Dirichlet Allocation (LDA) to classify documents and text into topics


In this recipe, we will explore the Latent Dirichlet Allocation (LDA) algorithm in Spark 2.0. The LDA we use in this recipe is completely different from linear analysis. Both Latent Dirichlet Allocation and linear discrimination analysis are referred to as LDA, but they are extremely different techniques. In this recipe, when we use the LDA, we refer to Latent Dirichlet Allocation. The chapter on text analytics is also relevant to understanding the LDA.

LDA is often used in natural language processing which tries to classify a large body of document (for example, emails from the Enron fraud case) into a discrete number of topics or themes so it can be understood. LDA is also a good candidate for selecting articles based on one's interest (for example, as you turn a page and spend time on a specific topic) in a given magazine article or page.

How to do it...

  1. Start a new project in IntelliJ or in an IDE of your choice...

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