This DP-100 Azure Machine Learning course is designed for data scientists and machine learning engineers who want to design and implement end-to-end machine learning solutions on Microsoft Azure while preparing for the DP-100 certification exam. The course provides a structured learning path that combines theoretical knowledge with practical Azure Machine Learning workflows.
Students will begin by learning how to design a machine learning solution and set up an Azure Machine Learning workspace. The course then explores data exploration, experiment tracking, and environment configuration, helping learners understand how to manage the full ML lifecycle in Azure.
A major focus of the course is model training optimization, where students learn how to improve performance, tune models, and manage experiments efficiently. The course also covers model management, enabling learners to track, evaluate, and compare different models within Azure Machine Learning.
In addition, students will learn how to deploy and consume machine learning models in real-world applications, ensuring models can be used in production environments. The course also includes dedicated DP-100 exam preparation episodes covering design, data handling, training, and deployment concepts.
By the end of the course, learners will be able to confidently design, build, and deploy machine learning solutions in Azure while being fully prepared for the DP-100 certification exam and real-world data science roles.
