Interpretable machine learning applications: Part 5
Completed by Marcus Blatter
June 5, 2025
1 hours (approximately)
Marcus Blatter's account is verified. Coursera certifies their successful completion of Interpretable machine learning applications: Part 5
What you will learn
 Be acquainted with the basics of the Aequitas Tool as a tool to measure and detect bias in the outcome of a machine learning prediction model.
Learn more about a real world case study, i.e., predictions of recidivism (COMPAS dataset), and how the prediction model may have been biased.
Learn a technique, which is largely based on statistical descriptors, for measuring bias and fairness for Machine Learning (ML) prediction models.
Skills you will gain
- Category: Model Evaluation
- Category: Exploratory Data Analysis
- Category: Responsible AI
- Category: Data Science
- Category: Predictive Analytics
- Category: Policy Analysis
- Category: Software Engineering
- Category: Descriptive Statistics
- Category: Data Ethics
- Category: Economics, Policy, and Social Studies
- Category: Histogram
- Category: Development Environment
