Microsoft Azure Machine Learning Fundamentals
3h 18mBeginner2023-04-20
Authors

Microsoft Press
Microsoft

Justin Frébault
Data Solutions Architect and Microsoft Certified Trainer
Course details
Machine learning is rapidly becoming ubiquitous, making it a key technology to learn. It is changing the landscape of business. Learning the key concepts of machine learning is essential to understanding its capabilities and knowing how to use it. This course targets those hands-on skills and provides directed learning in several important areas. Instructor Justin Frébault walks you through machine learning fundamentals—the different types of algorithms, the machine learning workflow, and data-centric machine learning—and provides best practices for better models with Azure Machine Learning. Learn how to industrialize your models by deploying and monitoring them through demos and hands-on labs. This course also introduces popular tools in Azure for interpreting your models so that they can better support business decisions.
Skills covered
Cloud DevelopmentMachine LearningAzureNetwork AdministrationCloud PlatformsArtificial Intelligence (AI)Network and System AdministrationCloud ComputingMicrosoftOne-Off
Concepts
0. Introduction
- 01 - Microsoft Azure Machine Learning Fundamentals - Introduction
1. Introduction to Machine Learning
- 02 - Learning objectives
- 03 - Understand machine learning
- 04 - Explore the machine learning workflow
- 05 - Learn how to select the right algorithms
- 06 - Discover data-centric machine learning
- 07 - Demo - Create an Azure machine learning workspace
- 08 - Lab - Create an Azure machine learning workspace
2. Introduction to Azure Machine Learning
- 09 - Learning objectives
- 10 - What is Azure machine learning
- 11 - Azure machine learning in context
- 12 - Azure machine learning workspaces
- 13 - Azure Machine Learning Studio
- 14 - Leverage the Azure Machine Learning SDK
- 15 - Demo - Exploring the Azure Machine Learning designer
- 16 - Lab - Running experiments with SDK
3. Improve Your Azure Machine Learning Model
- 17 - Learning objectives
- 18 - Hyperparameter tuning
- 19 - Automated machine learning
- 20 - Introduction to bias variance trade-off
- 21 - Demo - Automated machine learning
- 22 - Lab - Tuning hyperparameters
4. Deploy and Monitor Your Model
- 23 - Learning objectives
- 24 - Deploy and consume your model
- 25 - CI CD with machine learning
- 26 - Monitor data drift and use Application Insights
- 27 - Demo - Deploy your model and monitor with Application Insights
- 28 - Lab - Monitor data drift
5. Interpret Your Model
- 29 - Learning objectives
- 30 - Introduction to explainers
- 31 - Global and local feature importance
- 32 - Detect and mitigate fairness
- 33 - Demo - Interpret your model
- 34 - Lab - Detect and mitigate unfairness
Summary
- 35 - Microsoft Azure Machine Learning Fundamentals - Summary
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