This Azure Machine Learning Studio course is designed for learners who want to build practical machine learning skills using Microsoft Azure’s visual development environment. The course focuses on transforming raw data into meaningful insights and building predictive models using Azure ML Studio.
Students will begin by learning how to upload and manage datasets within Azure Machine Learning Studio. The course then introduces essential data preprocessing techniques such as summarizing data, handling missing values, cleaning datasets, and normalizing data to improve model performance.
A key focus of the course is predictive modeling, where learners explore how to build and use machine learning models for real-world predictions. The course also demonstrates how to publish models to the Azure gallery and integrate predictions into tools like Excel for business use cases.
In addition, students will learn how to expose machine learning models as APIs and test them using tools like Postman and C#, enabling real-world application integration. The course also covers advanced feature engineering techniques such as filter-based feature selection, permutation feature importance, and principal component analysis (PCA) to improve model accuracy and reduce complexity.
By the end of the course, learners will be able to prepare data, build predictive models, optimize features, and deploy machine learning solutions using Azure ML Studio confidently.
