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Machine Learning with Spark

You're reading from   Machine Learning with Spark Develop intelligent, distributed machine learning systems

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781785889936
Length 532 pages
Edition 2nd Edition
Languages
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Authors (2):
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Manpreet Singh Ghotra Manpreet Singh Ghotra
Author Profile Icon Manpreet Singh Ghotra
Manpreet Singh Ghotra
Rajdeep Dua Rajdeep Dua
Author Profile Icon Rajdeep Dua
Rajdeep Dua
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Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Up and Running with Spark FREE CHAPTER 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

Machine learning pipeline with an example

As discussed in the previous sections, one of the biggest features in the new ML library is the introduction of the pipeline. Pipelines provide a high-level abstraction of the machine learning flow and greatly simplify the complete workflow.

We will demonstrate the process of creating a pipeline in Spark using the StumbleUpon dataset.

The dataset used here can be downloaded from http://www.kaggle.com/c/stumbleupon/data.
Download the training data (train.tsv)--you will need to accept the terms and conditions before downloading the dataset. You can find more information about the competition at http://www.kaggle.com/c/stumbleupon.

Here is a glimpse of the StumbleUpon dataset stored as a temporary table using Spark SQLContext:

Here is a visualization of the StumbleUpon dataset:

...
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