About Microsoft Azure Machine Learning for Data Scientists course
Machine learning is at the core of artificial intelligence, and many modern applications and services depend on predictive machine learning models. Training a machine learning model is an iterative process that requires time and computational resources. Automated machine learning can help make it easier. In this course, you will learn how to use Azure Machine Learning to build and publish models without writing code. This is the second course in a five-course program that prepares you to pass the DP-100 exam: The certification exam is an opportunity to validate your knowledge and experience with machine learning solutions at cloud scale using Azure Machine Learning. This specialization will teach you how to use your existing Python and machine learning knowledge to manage data entry and preparation, model training and deployment, and monitoring machine learning solutions in Microsoft Azure. Each course teaches you the concepts and skills that are measured on the exam.
This specialization is designed for data scientists with knowledge of Python and machine learning frameworks such as Scikit-Learn, PyTorch, and Tensorflow who want to build and operate machine learning solutions in the cloud. This course teaches data scientists how to build end-to-end solutions in Microsoft Azure. Students will learn how to manage Azure resources for machine learning; run experiments and train models; deploy and operationalize machine learning solutions; and implement responsible machine learning. They will also learn how to use Azure Databricks for data exploration, preparation, and modeling, and how to integrate Databricks machine learning workflows with Azure Machine Learning.
