Free Code Fridays: Parallel and Iterative Processing for Machine Learning Recommendations with Spark

Video created by maprcommunity Employee on Feb 14, 2017

    Recommendation systems help narrow your choices to those that best meet your particular needs. They are among the most popular applications of big data processing. In this Free Code Friday session, you’ll learn how to build a recommendation model from movie ratings using an iterative algorithm and parallel processing with Apache Spark MLlib.

     

    Carol McDonald will cover:

     

    + A key difference between Apache Spark and MapReduce, which makes Spark much faster for iterative algorithms,
    + Loading and exploring the sample data set with Spark,
    + Using Spark MLlib’s Alternating Least Squares algorithm to make movie recommendations, and
    + Testing the results of the recommendations.

     

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    Apache Spark