Advanced Machine Learning with Spark 2.x

Course details

The aim of this course is to provide a practical understanding of advanced Machine Learning algorithms in Apache Spark to make predictions and recommendation and derive insights from large distributed datasets. This course starts with an introduction to the key concepts and data types that are fundamental to understanding distributed data processing and Machine Learning with Spark.

Further to this, we provide practical recipes that demonstrate some of the most popular algorithms in Spark, leading to the creation of sophisticated Machine Learning pipelines and applications. The final sections are dedicated to more advanced use cases for Machine Learning: streaming, Natural Language Processing, and Deep Learning. In each section, we briefly establish the theoretical basis of the topic under discussion and then cement our understanding with practical use cases.

About the Authors

Emmanuel Asimadi is a data scientist and works for projects at the University of Leicester.

Tomasz Lelek is a Software Engineer who programs mostly in Java and Scala. He is a fan of microservice architectures and functional programming. He dedicates considerable time and effort to be better every day. Recently, he's been delving into big data technologies such as Apache Spark and Hadoop. He is passionate about nearly everything associated with software development.

Tomasz thinks that we should always try to consider different solutions and approaches to solving a problem. Recently, he was a speaker at several conferences in Poland - Confitura and JDD (Java Developer's Day) and also at Krakow Scala User Group.

He also conducted a live coding session at Geecon Conference. He is currently working on this website using ML: