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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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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

Identifying data sources for practical machine learning

Getting data for machine learning projects was a challenge in the past. However, now there is a rich set of public data sources specifically suitable for machine learning.

Getting ready

In addition to the university and government sources, there are many other open sources of data that can be used to learn and code your own examples and projects. We will list the data sources and show you how to best obtain and download data for each chapter.

How to do it...

The following is a list of open source data worth exploring if you would like to develop applications in this field:

  • UCI machine learning repository: This is an extensive library with search functionality. At the time of writing, there were more than 350 datasets. You can click on the https://archive.ics.uci.edu/ml/index.html link to see all the datasets or look for a specific set using a simple search (Ctrl + F).
  • Kaggle datasets: You need to create an account, but you can download any sets for learning as well as for competing in machine learning competitions. The https://www.kaggle.com/competitions link provides details for exploring and learning more about Kaggle, and the inner workings of machine learning competitions.
  • MLdata.org: A public site open to all with a repository of datasets for machine learning enthusiasts.
  • Google Trends: You can find statistics on search volume (as a proportion of total search) for any given term since 2004 on http://www.google.com/trends/explore.
  • The CIA World Factbook: The https://www.cia.gov/library/publications/the-world-factbook/ link provides information on the history, population, economy, government, infrastructure, and military of 267 countries.

See also

Other sources for machine learning data:

There are some specialized datasets (for example, text analytics in Spanish, and gene and IMF data) that might be of some interest to you:

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Apache Spark 2.x Machine Learning Cookbook
Published in: Sep 2017
Publisher: Packt
ISBN-13: 9781783551606
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