• Online, Self-Paced
Course Description

Azure Machine Learning workspaces provide an environment for performing experiments and managing data, computer targets, and other assets. Other assets can include notebooks, pipelines, and trained models. This course will focus on using the Azure Machine Learning SDK.

In this course, you'll learn to create an Azure Machine Learning workspace by creating a machine learning resources, creating compute resources, and cloning a notebook. Next, you'll examine how to install the Machine Learning SDK for Python and create code to connect to a workspace. You'll learn to create Python scripts to run an experiment, log metrics, and retrieve and view logged metrics. Finally, you'll examine how to use the Azure Machine Learning SDK to run code experiments, create a script to train a model, and run a notebook using Jupyter to train predictive models.

This course is one in a collection that prepares learners for the Designing and Implementing a Data Science Solution on Azure (DP-100) exam.

Learning Objectives

{"discover the key concepts covered in this course"}

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Network Services