[Book Discussion] - Practical Machine Learning: Innovations in Recommendation

Discussion created by maprcommunity Employee on Nov 10, 2016


 Machine Learning is a critical tool used for gaining actionable insight, more accurate foresight, and relevant inferences into your ever-increasing amount of data. A widespread application of machine learning is the recommendation engine. Apache Mahout, a project to build scalable machine learning libraries, greatly simplifies the process of extracting recommendations and relationships from datasets.

In this guide, Practical Machine Learning: Innovations in Recommendation, authors and Mahout committers Ted Dunning and Ellen Friedman, shed light on a more approachable recommendation engine design and the business advantages for leveraging this innovative implementation style.

Download this latest guide from O’Reilly to learn:

  • A simplified approach for building effective recommender systems
  • Innovative use of search technology to deploy a recommendation engine at scale
  • Tips and tricks to ingest data in real-time and improve recommenders



Add a comment below. What did you think about this book?, What was your favorite part?, What would you like to read next about?

Ellen friedman andTed Dunning are eager to know your thoughts.


Haven't read the book yet?, find it here Practical Machine Learning 

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