Coursera Project Network
Interpretable Machine Learning Applications: Part 4
Coursera Project Network

Interpretable Machine Learning Applications: Part 4

Taught in English

Included with Coursera Plus

Guided Project

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

Intermediate level

Recommended experience

1.5 hours
Learn at your own pace
No downloads or installation required
Only available on desktop
Hands-on learning

What you'll learn

  • Set up a machine learning application in a "zero configuration" environment such as Google's Colab(oratory) Research platform.

  • Set up and configure the What-If Tool to analyze the behavior of exemplary machine learning prediction models.

Details to know

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Guided Project

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

Intermediate level

Recommended experience

1.5 hours
Learn at your own pace
No downloads or installation required
Only available on desktop
Hands-on learning

See how employees at top companies are mastering in-demand skills

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Learn, practice, and apply job-ready skills in less than 2 hours

  • Receive training from industry experts
  • Gain hands-on experience solving real-world job tasks
  • Build confidence using the latest tools and technologies
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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. Set up the environment for the "What-If" tool (WIT) as an extension in Jupyter and as a Google's Colaboratory notebook, including importing of the dataset (e.g., white wine quality data)

  2. Train classifiers, e.g., Decision Tree and Random Forest, as exemplary machine learning  prediction models to make predictions about the quality of white wines.

  3. Launch the What-If Tool (WIT) widget. This task will allow us to get a first understanding on how our prediction model(s) behave at both individual and global levels.

  4. Use the What-If Tool (WIT) features to explain the behavior of a prediction model on an individual basis.

  5. Use the What-If Tool (WIT) advanced features to explain the behavior of a prediction model on an individual basis.

  6. Use the What-If Tool (WIT) features to explain the behavior of a prediction model on a global basis.

Recommended experience

Some basic knowledge on machine learning, statistics and data science.

3 project images

Instructor

Epaminondas Kapetanios
Coursera Project Network
5 Courses3,067 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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