Coursera Project Network
Interpretable machine learning applications: Part 5

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Coursera Project Network

Interpretable machine learning applications: Part 5

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Learn, practice, and apply job-ready skills with expert guidance
4.4

(14 reviews)

Beginner level

Recommended experience

1.5 hours
Learn at your own pace
Hands-on learning
Learn, practice, and apply job-ready skills with expert guidance
4.4

(14 reviews)

Beginner level

Recommended experience

1.5 hours
Learn at your own pace
Hands-on learning

What you'll 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.

Details to know

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Taught in English
No downloads or installation required

Only available on desktop

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About this Guided Project

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Setting up the stage

  2. First attempt and stage for detecting bias

  3. Second attempt and stage for detecting bias

  4. Third attempt and stage in detecting bias

  5. Visualisation: Final stage for detecting bias

Recommended experience

Basic statistics, basic knowledge in machine learning and Python

5 project images

Instructor

Epaminondas Kapetanios
Coursera Project Network
5 Courses4,645 learners

Offered by

How you'll learn

  • Skill-based, hands-on learning

    Practice new skills by completing job-related tasks.

  • Expert guidance

    Follow along with pre-recorded videos from experts using a unique side-by-side interface.

  • No downloads or installation required

    Access the tools and resources you need in a pre-configured cloud workspace.

  • Available only on desktop

    This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.

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