This Azure Machine Learning 101 course is designed for complete beThis Azure Machine Learning 101 course is designed for complete beginners who want to understand how machine learning works in Microsoft Azure. The course provides a structured introduction to Azure ML and guides learners step-by-step through the essential concepts needed to build and deploy machine learning solutions.
Students will begin by learning the core foundations of Azure Machine Learning, including how the platform works and how it supports end-to-end machine learning workflows. The course then introduces different types of compute resources in Azure ML, helping learners understand how to choose the right environment for training and running models.
A key focus of the course is Automated Machine Learning (AutoML), which allows users to build models without deep coding knowledge. Learners will also explore datasets and datastores using the Azure ML UI, making it easier to manage and organize data for machine learning projects.
In addition, the course covers model deployment, including how to publish trained models as web services and integrate them into tools like Power BI for real-world analytics use cases. Students will also be introduced to the Azure ML SDK, enabling code-first machine learning development.
By the end of the course, learners will have a strong understanding of Azure Machine Learning fundamentals and the ability to build, train, and deploy basic machine learning solutions in the cloud.ginners who want to understand how machine learning works in Microsoft Azure. The course provides a structured introduction to Azure ML and guides learners step-by-step through the essential concepts needed to build and deploy machine learning solutions.
Students will begin by learning the core foundations of Azure Machine Learning, including how the platform works and how it supports end-to-end machine learning workflows. The course then introduces different types of compute resources in Azure ML, helping learners understand how to choose the right environment for training and running models.
A key focus of the course is Automated Machine Learning (AutoML), which allows users to build models without deep coding knowledge. Learners will also explore datasets and datastores using the Azure ML UI, making it easier to manage and organize data for machine learning projects.
In addition, the course covers model deployment, including how to publish trained models as web services and integrate them into tools like Power BI for real-world analytics use cases. Students will also be introduced to the Azure ML SDK, enabling code-first machine learning development.
By the end of the course, learners will have a strong understanding of Azure Machine Learning fundamentals and the ability to build, train, and deploy basic machine learning solutions in the clo
