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While machine learning has been used for decades, accessibility to these methods is undergoing a radical shift, with the rise of simple interfaces and implementations on distributed systems. In practice it means that more players can afford to take advantage of Machine Learning, and at larger scales. In this talk you will discover some introductory Machine Learning concepts and principles and illustrate them with use cases involving large amounts of data.
Based on simple examples put into a business context, and by using the Spark Notebook and Scala, you will learn how to apply different Machine Learning methods, using Apache Spark as the distributed processing engine.
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Xavier started his career as a researcher in Experimental Physics, and also focused on data processing. Further down the road, he took part in projects in finance, genomics, and software development for academic research. During that time, he worked on timeseries, on the prediction of biological molecular structures and interactions, and applied Machine Learning methodologies. He developed solutions to manage and process data distributed across data centres. He founded and now works at Data Fellas, a company dedicated to distributed computing and advanced analytics, leveraging Scala, Spark, and other distributed technologies.