Evaluating Model Effectiveness in Microsoft Azure
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This course is intended for data science practitioners who work with Azure Machine Learning Service and who seek to improve their ML model accuracy, efficiency, and explainability.
Data science and machine learning professionals work tirelessly to improve the quality of their ML models. In this course, Evaluating Model Effectiveness in Microsoft Azure, you will learn how to use Azure Machine Learning Studio to improve your models. First, you will learn how to evaluate model effectiveness in Azure. Next, you will discover how to improve model performance by eliminating overfitting and implementing ensembling. Finally, you will explore how to assess ML model interpretability. When you are finished with this course, you will have the skills and knowledge of Azure Machine Learning needed to ensure your ML models are consistent, accurate, and explainable.
Author Name: Tim Warner
Author Description:
Timothy Warner is a Microsoft Most Valuable Professional (MVP) in Cloud and Datacenter Management who is based in Nashville, TN. His professional specialties include Microsoft Azure, cross-platform PowerShell, and all things Windows Server-related. You can reach Tim via Twitter (@TechTrainerTim), LinkedIn or his blog, AzureDepot.com.
Table of Contents
- Course Overview
0mins - Evaluating Model Effectiveness
21mins - Improving Model Performance
13mins - Assessing Model Explainability
14mins
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