Course info
Dec 19, 2019

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.

About the author
About the author

Timothy Warner is a Microsoft Most Valuable Professional (MVP) in Cloud and Datacenter Management who is based in Nashville, TN.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi everyone. My name is Tim Warner. Welcome to my course, Evaluating Model Effectiveness in Microsoft Azure. I'm a Pluralsight staff author, Microsoft MVP, and Microsoft Azure Solution Architect. This intermediate-level course is for data science practitioners who work with Azure Machine Learning service and who seek to improve their ML model effectiveness. By the end of the course, you'll understand how to use Azure machine learning tools to evaluate model effectiveness and prove model performance and assess model explainability. I hope you'll join me on this journey to master the Azure machine learning model development process in our Evaluating Model Effectiveness in Microsoft Azure course, at Pluralsight.