Interpretable Machine Learning Applications: Part 4

Offered By
In this Guided Project, you will:

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.

1.5 hours
Intermediate
No download needed
Split-screen video
English
Desktop only

In this 1-hour long guided project, you will learn how to use the "What-If" Tool (WIT) in the context of training and testing machine learning prediction models. In particular, you will learn a) how to set up a machine learning application in Python by using interactive Python notebook(s) on Google's Colab(oratory) environment, a.k.a. "zero configuration" environment, b) import and prepare the data, c) train and test classifiers as prediction models, d) analyze the behavior of the trained prediction models by using WIT for specific data points (individual basis), e) moving on to the analysis of the behavior of the trained prediction models by using WIT global basis, i.e., all test data considered. 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

  • Data Analysis

  • Data scientist

  • Machine learning project management

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.

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

By purchasing a Guided Project, you'll get everything you need to complete the Guided Project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.

Because your workspace contains a cloud desktop that is sized for a laptop or desktop computer, Guided Projects are not available on your mobile device.

Guided Project instructors are subject matter experts who have experience in the skill, tool or domain of their project and are passionate about sharing their knowledge to impact millions of learners around the world.

You can download and keep any of your created files from the Guided Project. To do so, you can use the “File Browser” feature while you are accessing your cloud desktop.

Guided Projects are not eligible for refunds. See our full refund policy.

Financial aid is not available for Guided Projects.

Auditing is not available for Guided Projects.

At the top of the page, you can press on the experience level for this Guided Project to view any knowledge prerequisites. For every level of Guided Project, your instructor will walk you through step-by-step.

Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.

You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.