The Azure Machine Learning SDK provides components to quantity the importance of features, identify bias in models, and determine differential privacy. In this course, you'll learn more about these features and how they can be used to increase the quality of your machine learning models.
First, you'll examine how models can use global and local features to quantify the importance of each model feature. You'll explore how model explainers can be created using the Azure Machine Learning SDK and how to visualize the model using the Azure Machine Learning Studio. Next, you'll learn how to use a Jupyter Notebook and Python to generate explanations that are part of a model training experiment. Finally, you'll learn about training model bias and how to analyze model fairness using the Fairlearn Python package to detect and mitigate unfairness in a trained model.
This course is one in a collection that prepares learners for the Designing and Implementing a Data Science Solution on Azure (DP-100) exam.
Learning Objectives
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