Information Technology
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Evaluating Model Effectiveness in Microsoft Azure

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Course Features

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Duration

50 minutes

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Delivery Method

Online

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Available on

Downloadable Courses

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Accessibility

Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

50 minutes

Course Description

Machine learning and data science professionals are constantly striving to improve the quality their ML models. This course, Evaluating Model Effectiveness within Microsoft Azure, will teach you how to use Azure machine learning studio to improve your models. You will first learn how Azure evaluates model effectiveness. Next, you'll learn how to improve model performance through eliminating overfitting and implementing Ensembling. You will also learn how to evaluate ML model interpretationability. This course will equip you with the knowledge and skills required to ensure that your ML models are consistent and accurate.

Course Overview

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International Faculty

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Post Course Interactions

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Instructor-Moderated Discussions

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Case Studies, Captstone Projects

Skills You Will Gain

What You Will Learn

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

Course Instructors

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Tim Warner

Instructor

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 P...
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