Interpretable Machine Learning Applications: Part 1

Offered By
Coursera Project Network
In this Guided Project, you will:

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.

Clock2-hour course, including time of video recordings, practicing and readings, taking the quiz.
BeginnerBeginner
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this 1-hour long project-based course, you will learn how to create interpretable machine learning applications on the example of two classification regression models, decision tree and random forestc classifiers. You will also learn how to explain such prediction models by extracting the most important features and their values, which mostly impact these prediction models. In this sense, the project will boost your career as Machine Learning (ML) developer and modeler in that you will be able to get a deeper insight into the behaviour of your ML model. The project will also benefit your career as a decision maker in an executive position, or consultant, interested in deploying trusted and accountable ML applications. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

Python basic knowledgeFeatures engineeringMachine learning classification (regression) models

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.

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

More questions? Visit the Learner Help Center.