Cloud Data Science: Introduction to Azure Machine Learning


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level


Overview/Description

Explore Azure Machine Learning and how to create workspaces, projects, and experiments.



Expected Duration (hours)
1.4

Lesson Objectives

Cloud Data Science: Introduction to Azure Machine Learning

  • describe Machine Learning in the Microsoft Azure cloud platform
  • use Azure Machine Learning Studio and identify its capabilities
  • describe the four popular families of algorithms in Azure Machine Learning Studio
  • create an Azure Machine Learning Studio workspace
  • manage your workspace in the Azure portal by inviting users and switching between workspaces
  • use an Azure Resource Manager (ARM) deployment template to deploy a workspace
  • create a Jupyter notebook
  • add a sample dataset that's included in your workspace
  • add the Select Columns in Dataset module to specify which columns you want to include in your model
  • connect the Clean Missing Data module to the Select Columns in Dataset module
  • split the data for ML training, testing, and evaluation, and apply a learning algorithm to your model
  • use the Score Model and Evaluate Model modules to determine how well your model functions
  • work with the R programming language within Azure Machine Learning Studio
  • create an experiment in Azure Machine Learning Studio

    • Course Number:
    it_dfpdsm_01_enus

    Expertise Level
    Expert