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Mastering Apache Spark 2.x

You're reading from   Mastering Apache Spark 2.x Advanced techniques in complex Big Data processing, streaming analytics and machine learning

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781786462749
Length 354 pages
Edition 2nd Edition
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Author (1):
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Romeo Kienzler Romeo Kienzler
Author Profile Icon Romeo Kienzler
Romeo Kienzler
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Table of Contents (15) Chapters Close

Preface 1. A First Taste and What’s New in Apache Spark V2 FREE CHAPTER 2. Apache Spark SQL 3. The Catalyst Optimizer 4. Project Tungsten 5. Apache Spark Streaming 6. Structured Streaming 7. Apache Spark MLlib 8. Apache SparkML 9. Apache SystemML 10. Deep Learning on Apache Spark with DeepLearning4j and H2O 11. Apache Spark GraphX 12. Apache Spark GraphFrames 13. Apache Spark with Jupyter Notebooks on IBM DataScience Experience 14. Apache Spark on Kubernetes

Apache Spark MLlib

MLlib is the original machine learning library that is provided with Apache Spark, the in-memory cluster-based open source data processing system. This library is still based on the RDD API. In a later chapter, we'll also learn how machine learning on the newer DataFrame and Dataset API works. In this chapter, we will examine the functionality provided with the MLlib library in terms of areas such as regression, classification, and neural network processing. We will examine the theory behind each algorithm before providing working examples that tackle real problems. The example code and documentation on the web can be sparse and confusing.

We will take a step-by-step approach in describing how the following algorithms can be used and what they are capable of doing:

  • Architecture
  • Classification with Naive Bayes
  • Clustering with K-Means
  • Image classification...
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