Evaluating Model Effectiveness in Microsoft Azure

Focused View

Tim Warner

50:50

20 View

01 - Course Overview

01 - Course Overview.mp4

00:47

02 - Evaluating Model Effectiveness

02 - Overview.mp4

01:11

03 - Preliminary Terminology.mp4

02:40

04 - Scoring and Evaluating an Azure ML Pipeline.mp4

03:57

05 - Demo - Inspecting an Azure ML Pipeline.mp4

07:17

06 - Demo - Scoring and Evaluating the Pipeline Model.mp4

05:29

07 - Summary.mp4

01:13

03 - Improving Model Performance

08 - Overview.mp4

00:42

09 - Detecting and Preventing Overfitting.mp4

02:34

10 - Azure Automated Machine Learning.mp4

01:38

11 - Demo - Creating an Automated ML Experiment.mp4

06:01

12 - Demo - Interpreting the Experiment Results.mp4

01:34

13 - Summary.mp4

01:13

04 - Assessing Model Explainability

14 - Overview.mp4

00:23

15 - Microsofts Guiding Principles for Responsible AI.mp4

02:19

16 - Unintended Bias and Interpretability.mp4

02:39

17 - Setting Up an Experiment in a Jupyter Notebook.mp4

04:07

18 - Demo - Touring the Azure Python Interpretability SDK.mp4

03:57

19 - Summary.mp4

01:09

Description

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.

What You'll Learn?

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.

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Microsoft Azure

Data Modeling

Tim Warner

Instructor's Courses

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.

Pluralsight

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Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.