Coursera Project Network
Interpretable Machine Learning Applications: Part 1
Coursera Project Network

Interpretable Machine Learning Applications: Part 1

Taught in English

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Guided Project

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

Beginner level

Recommended experience

2-hour course, including time of video recordings, practicing and readings, taking the quiz.
Learn at your own pace
No downloads or installation required
Only available on desktop
Hands-on learning
4.3

(27 reviews)

What you'll learn

  • How to select and compare different prediction models (classification regressors) for a real world dataset (FIFA 2018 Soccer World Cup Statistics).

  • How to extract the most important features, which impact the classifiers, in a model-agnostic approach, together with caveats.

  • How to get an insight into the way values of the most important features impact the predictions made by the classifiers.

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Guided Project

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

Beginner level

Recommended experience

2-hour course, including time of video recordings, practicing and readings, taking the quiz.
Learn at your own pace
No downloads or installation required
Only available on desktop
Hands-on learning
4.3

(27 reviews)

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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 the stage (Python Jupyter Lab web-based Server environment, importing the dataset and file to train and test the designated classification regressors as prediction models).

  2. Train, test and estimate the accuracy (confusion matrix) of a Decision Tree classifier.

  3. Train, test and estimate the accuracy (confusion matrix) of a Random Tree classifier as an alternative to the previous one.

  4. Extract a ranking list of the features, which are most important for each one of our prediction models.

  5. Extract and plot the impact of the values of selected important features on predictions being made by each one of our prediction models.

Recommended experience

Molnar, C.: Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, https://christophm.github.io/interpretable-ml-book/

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Instructor

Epaminondas Kapetanios
Coursera Project Network
5 Courses3,119 learners

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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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5

Reviewed on Aug 6, 2022

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