This beginner-focused course introduces Azure Machine Learning Studio and provides a practical starting point for building machine learning models on Microsoft Azure. The course guides learners through creating an Azure Machine Learning instance, navigating Azure ML Studio, and managing compute resources efficiently.

Learners explore how to work with Jupyter Notebooks inside Azure ML Studio, enabling interactive experimentation and model development directly in the cloud. A key focus of the course is Automated Machine Learning (AutoML), where learners build and evaluate classification models with minimal coding. The course explains how to interpret AutoML results, understand confusion matrices, and analyze common classification metrics to assess model performance.

In addition to UI-based workflows, the course introduces AutoML using Python and the Azure ML SDK, helping learners transition from visual tools to programmatic control. Data handling is demonstrated through uploading external datasets, including Kaggle datasets, into Azure Machine Learning Studio.

Real-world case studies are used to reinforce learning, showing how machine learning can be applied to fun and practical problems such as predicting NHL penalties and analyzing movie classification debates. This course is ideal for beginners, analysts, and aspiring data scientists who want a practical introduction to Azure Machine Learning.