Microsoft Azure Machine Learning Fundamentals (Video)

3+ Hours of Video Training on Machine Learning Fundamentals and Best Practices for Better Models with Azure Machine Learning

In this video training, Justin Frébault walks you through Machine Learning fundamentals and provides best practices for better models with Azure Machine Learning. Through the use of demos and hands-on labs, you will learn how to industrialize your models by deploying and monitoring them. This course will also introduce popular tools in Azure for interpreting your models so that they can better support business decisions.

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 will provide directed learning in several important areas including the basics of Machine Learning, which covers the different types of algorithms, the Machine Learning workflow and data-centric Machine Learning. It will also cover deploying and monitoring models, as well as the important skill of interpreting models.

Topics include:

Lesson 1: Introduction to Machine Learning

Lesson 1 introduces what Machine Learning is as a discipline. This lesson discusses the Machine Learning workflow and cover how to select the right algorithms and what data centric Machine Learning is. We will then start our first exercises and create an Azure Machine Learning workspace.

Lesson 2: Introduction to Azure Machine Learning

This lesson introduces Azure Machine Learning and discusses the different components of the platform as well as its audience. We will go deeper into what constitutes a Workspace, and then explore the Studio and the SDK. The goal will be to run our first experiments in the lab.

Lesson 3: Improve Your Azure Machine Learning Model

This lesson will cover how to improve your Models. We will see two technics: hyperparameter tunning and automated Machine Learning. A key aspect will be to learn how to balance bias and variance, and the underlying principle of the bias/variance tradeoff. The lesson will finish with a lab where we will tune hyperparameters.

Lesson 4: Deploy and Monitor Your Model

Here we will talk about the important aspects of deploying and monitoring your models. This is an important lesson if you want to productionize your work. We will see how to increase the quality of your solution with CI/CD, and what your options are to monitor data drift and models.

Lesson 5: Interpret Your Model

In this final lesson we will cover interpretability in Machine Learning. We will see why it is important and what explainers are. We will address global and local feature importance. We will also touch on fairness: how to evaluate it and ensure it.

Skill Level:

• Beginner

Learn How To:

• Select the right algorithm for your business case
• Improve your models with hyperparameter tunning and automated ML
• Industrialize your models with deployment and monitoring
• Interpret your models to support business decisions

Who Should Take This Course:

• IT Professionals who want to understand the essence and potential of Machine Learning
• Data analysts or A.I. enthusiasts who would like to become a Data Scientist
• Beginner Data Scientists looking to reinforce Machine Learning concepts
• Anyone interested in learning how to industrialize your models with Azure Machine Learning

Course Requirements:

• Basic knowledge or Python preferred

About Pearson Video Training:

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Prentice Hall, Sams, and Que Topics include: IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at http://www.informit.com/video.

Video Lessons are available for download for offline viewing within the streaming format. Look for the green arrow in each lesson.

Table of Contents

Introduction

Lesson 1: Introduction to Machine Learning

Learning Objectives 

     1.1 Understand Machine Learning         

     1.2 Explore the Machine Learning Workflow   

     1.3 Learn How to Select the Right Algorithms

     1.4 Discover Data Centric Machine Learning    

     1.5 Demo: Create an Azure Machine Learning Workspace      

     1.6 Lab: Create an Azure Machine Learning Workspace

Lesson 2: Introduction to Azure Machine Learning

Learning Objectives 

     2.1 What Is Azure Machine Learning?   

     2.2 Azure Machine Learning in Context

     2.3 Azure Machine Learning Workspaces         

     2.4 Azure Machine Learning Studio       

     2.5 Leverage the Azure Machine Learning SDK

     2.6 Demo: Exploring the Azure Machine Learning Designer   

     2.7 Lab: Running Experiments with SDK

Lesson 3: Improve Your Azure Machine Learning Model

Learning Objectives 

     3.1 Hyperparameter Tuning       

     3.2 Automated Machine Learning

     3.3 Introduction to Bias/Variance Tradeoff      

     3.4 Demo: Automated Machine Learning

     3.5 Lab: Tuning Hyperparameters

Lesson 4: Deploy and Monitor Your Model

Learning Objectives

     4.1 Deploy and Consume Your Model

     4.2 CI/CD with Machine Learning

     4.3 Monitor Using Data Drift and Application Insights

     4.4 Demo: Deploy Your Model and Monitor with Application Insights          

     4.5 Lab: Monitor Data Drift

Lesson 5: Interpret Your Model

Learning Objectives 

     5.1 Introduction to Explainers   

     5.2 Global and Local Feature Importance         

     5.3 Detect and Mitigate Fairness

     5.4 Demo: Interpret Your Model

     5.5 Lab: Detect and Mitigate Unfairness

Summary

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