This course provides a hands-on introduction to Azure Machine Learning Studio with a strong focus on data preparation, feature engineering, and prediction workflows. It is designed for learners who want to understand how to transform raw data into meaningful machine learning models using Azure’s visual tools.

The course begins with uploading and managing datasets in Azure Machine Learning Studio, followed by essential data preprocessing techniques such as summarizing data, normalization, and handling missing values. Learners are then introduced to prediction concepts, enabling them to build and evaluate predictive models within the Azure ML Studio environment.

Beyond model creation, the course demonstrates how to publish experiments to the Azure ML Gallery and make trained models accessible for real-world use. Learners explore multiple ways to consume predictions, including integration with Excel and creating APIs that can be tested using Postman or consumed through C# applications.

Feature selection and dimensionality reduction are also covered in detail. Topics include filter-based feature selection, permutation feature importance, and Principal Component Analysis (PCA), along with practical examples using real survey data.

This course is ideal for data analysts, beginners in machine learning, and professionals who want to master data preparation and prediction using Azure Machine Learning Studio.