Building Machine Learning Models in Azure

Cloud-based machine learning is transforming how organizations make predictions and build intelligent systems. If you want to transition into data science, understanding how to leverage cloud environments to train and manage models is an essential modern skill. This written course guides you through the foundational concepts of machine learning and shows you how to implement them using Azure. You will progress from understanding core algorithms to configuring workspaces, training models, and exploring the basics of modern MLOps for automated workflows. What you'll learn: * Understand core machine learning concepts, terminology, and the model development lifecycle. * Configure and navigate the Azure Machine Learning workspace for data science projects. * Train and evaluate predictive models using automated machine learning and designer tools. * Deploy trained models as web services to make real-time predictions. * Apply basic MLOps principles to manage, monitor, and register your machine learning models. You will begin by learning fundamental machine learning theory before moving into hands-on configuration of cloud workspaces. Through written explanations and practical code snippets, you will learn to run experiments, evaluate performance metrics, and prepare models for production. This course is designed for beginners who are new to machine learning and cloud environments, requiring no prior data science experience. Start reading today to build your first machine learning model in the cloud.