Interpretable Machine Learning Applications: Part 4

Offert par
Coursera Project Network
Dans ce Projet Guidé, vous :

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
IntermediateIntermédiaire
CloudAucun téléchargement requis
VideoVidéo en écran partagé
Comment DotsAnglais
LaptopOrdinateur de bureau uniquement

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.

Les compétences que vous développerez

  • Data Analysis
  • Data scientist
  • Machine learning project management

Apprendrez étape par étape

Votre enseignant(e) vous guidera étape par étape, grâce à une vidéo en écran partagé sur votre espace de travail :

  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.

Comment fonctionnent les Projets Guidés

Votre espace de travail est un bureau cloud situé dans votre navigateur, aucun téléchargement n'est requis.

Votre enseignant(e) vous guide étape par étape dans une vidéo en écran partagé

Foire Aux Questions

Foire Aux Questions

D'autres questions ? Visitez le Centre d'Aide pour les Étudiants.