Interpretable Machine Learning Applications: Part 4

von
Coursera Project Network
In diesem Angeleitetes Projekt werden Sie:

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.

Clock1.5 hours
IntermediateMittel
CloudKein Download erforderlich
VideoVideo auf geteiltem Bildschirm
Comment DotsEnglisch
LaptopNur Desktop

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.

Kompetenzen, die Sie erwerben werden

Data AnalysisData scientistMachine learning project management

Schritt für Schritt lernen

In einem Video, das auf einer Hälfte Ihres Arbeitsbereichs abgespielt wird, führt Sie Ihr Dozent durch diese Schritte:

  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.

Ablauf angeleiteter Projekte

Ihr Arbeitsbereich ist ein Cloud-Desktop direkt in Ihrem Browser, kein Download erforderlich

Ihr Dozent leitet Sie in einem Video mit geteiltem Bildschirm Schritt für Schritt an.

Häufig gestellte Fragen

Häufig gestellte Fragen

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