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Interpretable machine learning applications: Part 5

By Coursera on Coursera · Data Science
Price
Free

About This 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.

Instructor

Epaminondas Kapetanios

Frequently Asked Questions

How much does Interpretable machine learning applications: Part 5 cost?
Visit the Interpretable machine learning applications: Part 5 course page for current pricing and available discounts.
Who teaches Interpretable machine learning applications: Part 5?
Interpretable machine learning applications: Part 5 is taught by Epaminondas Kapetanios, Coursera.
What skill level is Interpretable machine learning applications: Part 5 for?
This course is designed for all levels learners.