The focus of Part 1 is the Crowdsourcing paradigm. We start by introducing and discussing what Crowdsourcing is and how it can be used to obtain high-quality data for AI.

Then we zoom into both the mechanisms that can be used to control for quality as well as the human factors that affect the crowdsourced data.

This leads to the Lab activity for Part 1: taking data that has been labelled by crowd workers and performing data quality checks and cleaning operations. We recommend doing this hands-on activity even if you audit this course!

Throughout Part 1 there are interesting readings and additional video content to help you out during the learning experience.

This then leads to Part 2, where you will learn about using crowdsourced data for building and debugging AI-based solutions.