CloudAcademy – Introduction to Azure Machine Learning

This learning path will introduce you to the primary machine learning tools on Azure. Even if you don’t have any previous experience with machine learning, that’s okay, because the first course starts with an introduction to the basic concepts.

Machine learning is a notoriously complex subject, which usually requires a great deal of advanced math and software development skills. That’s why it’s so amazing that Azure Machine Learning Studio lets you train and deploy machine learning models without any coding, using a drag-and-drop interface. After taking the first course and the lab in this learning path, you will be able to create your own machine learning models using Azure Machine Learning Studio.

The second course will show you how to use Azure Databricks, a cloud-based implementation of Spark with a user-friendly interface for running code on clusters interactively. One of the biggest advantages of using the Azure version of Databricks is that it’s integrated with other Azure services. For example, you can train a machine learning model on a Databricks cluster and then deploy it using Azure Machine Learning Services.

Both of these courses include hands-on demos you can do yourself. Then you can test what you’ve learned by doing the lab and taking the exam.

Learning Objectives

• Prepare data for use by an Azure Machine Learning Studio experiment
• Train a machine learning model in Azure Machine Learning Studio
• Deploy a model trained in Azure Machine Learning Studio to make predictions
• Prepare data for use by an Azure Machine Learning Workbench experiment
• Train a machine learning model in Azure Machine Learning Workbench
• Deploy a model trained in Azure Machine Learning Workbench to make predictions

Prerequisites

• Python experience
• Microsoft Azure account (recommended)

Intended Audience

• Anyone interested in machine learning

Feedback

If you have thoughts or suggestions for this learning path, please contact Cloud Academy at [email protected].