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.

Style and Approach

The video course starts with an introduction to Machine Learning libraries, data types, and key concepts that apply to Machine Learning pipelines with Apache Spark. Then, via recipes including practical Kaggle examples, we will understand different components of Machine Learning applications and considerations in building, evaluating, and deploying various Machine Learning models. With an understanding of Machine Learning models gained in previous sections, we move on to deal with more sophisticated use-cases such as streaming using the structured streaming library in Spark 2.X. Other special use cases we deal with include Natural Language Processing and Deep Learning using external libraries with Spark.