CloudAcademy

Predict Income Levels Using Azure Machine Learning Studio

The hands-on lab is part of this learning path

Introduction to Azure Machine Learning
course-steps 2 certification 1 lab-steps 1

Lab Steps

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Logging into the Microsoft Azure Portal
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Connecting to Azure Machine Learning Studio
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Creating an Experiment to Predict Income Levels
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Performing the Experiment to Predict Income Levels
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Comparing to a Model Trained with a Subset of Features
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Deleting the Experiment

Ready for the real environment experience?

DifficultyBeginner
Duration50m
Students83

Description

Lab Overview

Microsoft Azure Machine Learning Studio is a drag-and-drop tool that allows you to visually and collaboratively build, test, and deploy machine learning models. In this Lab, you will predict income levels using census data and compare the performance of two trained models in Azure Machine Learning Studio. You will see how easy it is to build powerful models without having to write a single line of code!

Lab Objectives

Upon completion of this Lab you will be able to:

  • Build and evaluate machine learning models with Azure Machine Learning Studio Experiments
  • Compare models from within Azure Machine Learning Studio
  • Explore datasets visually using Azure Machine Learning Studio

Lab Prerequisites

You should be familiar with:

  • Navigating the Azure Portal

Lab Environment

Before completing the Lab instructions, the environment will look as follows:

After completing the Lab instructions, the environment should look similar to:

About the Author

Students10233
Labs68
Courses7
Learning paths4

Logan has been involved in software development and research since 2007 and has been in the cloud since 2012. He is an AWS Certified DevOps Engineer - Professional, MCSE: Cloud Platform and Infrastructure, Google Cloud Certified Associate Cloud Engineer, Certified Kubernetes Administrator (CKA), Certified Kubernetes Application Developer (CKAD), and Linux Foundation Certified System Administrator (LFCS). He earned his Ph.D. studying design automation and enjoys all things tech.