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Learning Salesforce Einstein

You're reading from   Learning Salesforce Einstein Add artificial intelligence capabilities to your business solutions with Heroku, PredictiveIO, and Force

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781787126893
Length 334 pages
Edition 1st Edition
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Author (1):
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Mohit Shrivatsava Mohit Shrivatsava
Author Profile Icon Mohit Shrivatsava
Mohit Shrivatsava
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Table of Contents (10) Chapters Close

Preface 1. Introduction to AI FREE CHAPTER 2. Role of AI in CRM and Cloud Applications 3. Building Smarter Apps Using PredictionIO and Heroku 4. Product Recommendation Application using PredicitionIO and Salesforce App Cloud 5. Salesforce Einstein Vision 6. Building Applications Using Einstein Vision and Salesforce Force.com Platform 7. Einstein for Analytics Cloud 8. Einstein and Salesforce IoT Cloud Platform 9. Measuring and Testing the Accuracy of Einstein

Introduction to Spark MLlib

MLlib is a part of the Spark project that provides machine learning capabilities. One of the reasons to choose MLlib is that it's built on Apache Spark, which is a fast and general engine for large-scale data processing. One can find extensive documentation on MLlib at http://spark.apache.org/docs/latest/ml-guide.html. MLlib out of the box provides machine learning algorithms, such as the following:

  • Classification: This is used by Gmail to categorize whether an email is spam or not.
  • Clustering: This is categorization. Google uses this to categorize news articles into various categories such as sports, politics, weather, and so on, based on the title and content.
  • Collaborative Filtering: This is used by the recommendation engines. YouTube and Amazon are classic examples for this as they recommend items based on likes and ratings from the user.
  • ...
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