Course Code: ML0101ENv3
Course Level: Intermediate
Time to Complete: 12 hours
In this course you will learn about basic statistics and data types, preparing data, feature engineering, fitting a model and pipelines and grid search. Apache Spark™ is a fast and general engine for large-scale data processing with built-in modules for streaming, machine learning and graph processing. This course shows you how to use Spark’s machine learning pipelines to fit models and search for optimal hyperparameters using a Spark cluster.
Petro Verkhogliad is Consulting Manager at Lightbend. He holds a Masters degree in Computer Science with specialization in Intelligent Systems. He is passionate about functional programming and applications of AI.
Dr Priya Dev is a lecturer of statistics at ANU and UNSW and also a founder of a mobile commerce startup, Qhopper. She completed a PhD in probability theory from ANU and Columbia University and has been a data analytics consultant to ASX listed companies and global banks. Qhopper is a massively scalable mobile commerce platform built on the Lightbend platform using Scala and Spark. It bridges the technology gap for hospitality businesses, helping them create better experiences and connect with new and existing customers through their own online ordering, CRM and business intelligence suite.
Joseph has a Ph.D. in Electrical Engineering. His research focuses on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.
Agatha Colangelo also contributed.