This DP-100 Azure Machine Learning workspace course is designed for learners preparing for the DP-100 certification and data science professionals who want to understand how to set up and configure Azure Machine Learning environments. The course focuses on building a strong foundation for working with machine learning workloads in Microsoft Azure.

Students will begin by learning how to create an Azure Machine Learning workspace, which serves as the central environment for managing datasets, experiments, models, and compute resources. The course then explores how to navigate the workspace and use different developer tools to interact with machine learning projects efficiently.

A key focus of the course is configuring the workspace for machine learning workloads. Learners will understand how to create and manage data assets, set up compute resources, and prepare the environment for training and running models in the cloud.

The course also introduces essential tools used in Azure Machine Learning, helping learners understand how to streamline data science workflows and improve productivity. It emphasizes practical usage of the workspace to support real-world machine learning projects and DP-100 exam scenarios.

By the end of the course, learners will be able to confidently create, configure, and manage Azure Machine Learning workspaces and understand how they support end-to-end data science solutions in Azure.