Jacob Parr introduces you to Apache Spark 2.0 core concepts with a focus on Spark’s machine learning library, using text mining on real-world data as the primary end-to-end use case. Join Jacob to explore and wrangle data using Spark’s DataSet and DataFrame abstractions. You’ll use the Spark ML API to build an ML pipeline to transform free text into useful features via Spark ML’s Transformer abstraction (e.g., one-hot encoding and term frequency counting) and learn about model selection, training and fitting, and validation and inspection, as well as parameter tuning with grid search parameter selection.
The class will consist of approximately 50% hands-on programming labs in Scala and 50% lecture and discussion.
Jacob Parr is a Databricks-certified Apache Spark instructor and assists Databricks with the development of its Apache Spark curriculum. Besides teaching Spark, Jacob delivers trainings to dozens of companies in topics spanning REST, microservices, Selenium, and big data. In his more than 20 years in software development, Jacob has developed applications in telecom, real estate, ecommerce, enterprise taxation, and more. Jacob has been a frequent guest speaker at Spark Summit and continues to speak and teach at events like Strata Data Conference. Happily married with three adult children, Jacob and his wife Angela love to travel around the US or simply cuddle up by a warm fire with their two Boston Terriers. In his spare time, Jacob plays practical jokes, flies drones, chases his nieces and nephews with an arsenal of Nerf guns, or simply piddles around with his N scale train sets.
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