November 25, 2019

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Distinguish pop music from heavy metal using Apache Spark MLlib

Distinguish pop music from heavy metal using Apache Spark MLlib

Taras Matyashovsky explains how to use Apache Spark MLlib to build a supervised learning NLP pipeline to distinguish pop music from heavy metaland have fun in the process.

Talk Title Distinguish pop music from heavy metal using Apache Spark MLlib
Speakers Taras Matyashovsky (Lohika)
Conference O’Reilly Open Source Convention
Conf Tag Making Open Work
Location Austin, Texas
Date May 8-11, 2017
URL Talk Page
Slides Talk Slides
Video

Machine learning may be overhyped nowadays, but there is still a strong belief that this area is exclusively for data scientists with a deep mathematical background who leverage the Python (scikit-learn, Theano, TensorFlow, etc.) or R ecosystems and use specific tools like R Studio, Matlab, or Octave. Obviously, there is some truth to this statement, but Java engineers can also take the best of the machine-learning world from an applied perspective by using our native language and familiar frameworks like Apache Spark. Taras Matyashovsky explains how to use Apache Spark MLlib to build a supervised learning NLP pipeline to distinguish pop music from heavy metal—and have fun in the process. Along the way, Taras offers an overview of the simplest machine-learning tasks and algorithms, like regression, classification, and clustering.

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