This Azure Machine Learning Studio course is designed for beginners who want to learn how to build and evaluate machine learning models using Microsoft Azure’s visual and code-based tools. The course provides a practical introduction to Azure ML Studio and guides learners through the full machine learning workflow from setup to model evaluation.
Students will begin by creating an Azure Machine Learning instance and understanding how to navigate Azure ML Studio. The course then covers compute management, allowing learners to configure resources for training and running machine learning models efficiently.
A key focus of the course is working with Jupyter Notebooks inside Azure ML Studio, enabling learners to build models using Python in a cloud environment. The course also introduces Automated Machine Learning (AutoML) for classification tasks, helping users build models without extensive coding knowledge.
Learners will explore how to interpret AutoML results, understand confusion matrices, and evaluate model performance using key classification metrics. The course also covers integrating Python with Azure ML SDK for more advanced AutoML workflows.
In addition, students will learn how to upload datasets (including Kaggle data), manage experiments, and analyze real-world machine learning cases. By the end of the course, learners will be able to build, evaluate, and interpret machine learning models using Azure ML Studio confidently.
