Home AI Courses Interpretable ml applications: Part 5

Interpretable ml applications: Part 5

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With an emphasis on fairness measurement methods, the course teaches students how to use the Aequitas Tool to identify bias in machine learning models.

Key AI Functions:software engineering,artificial intelligence (ai),data science

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Description for Interpretable ml applications: Part 5

Aequitas Tool Overview: Explore the Aequitas Tool's capabilities for identifying and quantifying bias in machine learning prediction models.
Case Study on Recidivism: Utilize the COMPAS dataset to investigate the potential effects of biases on outcomes and to make predictions regarding recidivism.
Bias and Fairness Measurement Technique: Investigate statistical methods for evaluating bias and fairness in machine learning models, regardless of the specific algorithms used.
Career Advancement: Acquire skills that are advantageous to not only data scientists and ML developers, but also to policymakers and decision makers who prioritize equitable AI applications.

Level: Beginner

Certification Degree: Yes

Languages the Course is Available: 22

Offered by: On Coursera provided by Coursera Project Network

Duration: 1.5 hours at your own pace

Schedule: Hands-on learning

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