Azure Data Scientist Associate: Model Features & Differential Privacy
Azure 2021
| Intermediate
- 10 videos | 1h 2m 4s
- Includes Assessment
- Earns a Badge
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.
WHAT YOU WILL LEARN
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discover the key concepts covered in this course
describe how learning models can use global and local features to quantify the importance of each feature
describe how model explainers can be created using the Azure Machine Learning SDK
create an explainer and upload the explanation so it is available later analysis
use a Jupyter Notebook and Python to generate explanations that are part of a model training experiment -
use visualizations in Azure Machine Learning Studio to visualize model explanations
describe how training models can be biased due to biases in the training data
analyze model fairness using the Fairlearn Python package to identify imbalances between predictions and prediction performance
use a Jupyter Notebook and Python to detect and mitigate unfairness in a trained model
summarize the key concepts covered in this course
IN THIS COURSE
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1m 38s
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5m 15s
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3. Machine Learning Model Explainers5m 42s
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4. Creating Machine Learning Explainers8m 22s
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5. Interpreting Machine Learning Models with Python7m 5s
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6. Visualizing Machine Learning Explanations7m 26s
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7. Machine Learning Model Bias7m
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8. Azure Machine Learning and Fairlearn7m 7s
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9. Detecting and Mitigating Model Fairness11m 40s
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10. Course Summary50s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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