Back to Interpretable machine learning applications: Part 5
Learner Reviews & Feedback for Interpretable machine learning applications: Part 5 by Coursera
20 ratings
About the Course
You will be able to use the Aequitas Tool as a tool to measure and detect bias in the outcome of a machine learning prediction model. As a use case, we will be working with the dataset about recidivism, i.e., the likelihood for a former imprisoned person to commit another offence within the first two years, since release from prison. The guided project will be making use of the COMPAS dataset, which already includes predicted as well as actual outcomes. Given also that this technique is largely based on statistical descriptors for measuring bias and fairness, it is very independent from specific Machine Learning (ML) prediction models. In this sense, the project will boost your career not only as a Data Scientists or ML developer, but also as a policy and decision maker.
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1 - 5 of 5 Reviews for Interpretable machine learning applications: Part 5
By Mohamed K
•Jun 20, 2021
Good
By Ashish S
•May 30, 2024
NA
By Saideep S
•Jun 30, 2025
Knowleadgable
By Pascal U E
•Jul 3, 2021
Good content, but hard to follow the instructor and do as he does
By BD C
•Nov 20, 2023
It was very difficult to follow the project because the instructor's window did not allow the complete script to be displayed (therefore, you can not type it into the jupyter notebook cell). Also, the rationale behind the steps performed was not provided. Lastly, the interpretation methodology was not provided or corelated to the course objectives.