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Machine Learning with Spark. - Second Edition

You're reading from  Machine Learning with Spark. - Second Edition

Product type Book
Published in Apr 2017
Publisher Packt
ISBN-13 9781785889936
Pages 532 pages
Edition 2nd Edition
Languages
Authors (2):
Rajdeep Dua Rajdeep Dua
Profile icon Rajdeep Dua
Manpreet Singh Ghotra Manpreet Singh Ghotra
Profile icon Manpreet Singh Ghotra
View More author details

Table of Contents (13) Chapters

Preface 1. Getting Up and Running with Spark 2. Math for Machine Learning 3. Designing a Machine Learning System 4. Obtaining, Processing, and Preparing Data with Spark 5. Building a Recommendation Engine with Spark 6. Building a Classification Model with Spark 7. Building a Regression Model with Spark 8. Building a Clustering Model with Spark 9. Dimensionality Reduction with Spark 10. Advanced Text Processing with Spark 11. Real-Time Machine Learning with Spark Streaming 12. Pipeline APIs for Spark ML

Types of machine learning models

While we have one example, there are many other examples, some of which we will touch on in the relevant chapters when we introduce each machine learning task.

However, we can broadly divide the preceding use cases and methods into two categories of machine learning:

  • Supervised learning: These types of models use labeled data to learn. Recommendation engines, regression, and classification are examples of supervised learning methods. The labels in these models can be user--movie ratings (for the recommendation), movie tags (in the case of the preceding classification example), or revenue figures (for regression). We will cover supervised learning models in Chapter 4, Building a Recommendation Engine with Spark, Chapter 6, Building a Classification Model with Spark, and Chapter 7, Building a Regression Model with Spark.
  • Unsupervised learning: When a model does not require labeled...
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