Certification     Microsoft     Microsoft Certified: Azure Data Scientist Associate     DP-100: Designing and Implementing a Data Science Solution on Azure

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Machine learning classification models are used to predict the class or category that an item belongs to. For example, using patient characteristics such as age, weight, and BMI to predict if they are at risk for specific diseases. In this course, you'll learn about using classification models in the Azure Machine Learning Studio.
You'll explore the available types of classification models and the steps required to train a classification model. Next, you'll learn the ideal metrics for determining the best classification model to use for the given data. Finally, you'll examine how to use an existing pipeline to create a new inference pipeline and create and deploy a predictive service for a classification model.
This course is one in a collection that prepares learners for the Designing and Implementing a Data Science Solution on Azure (DP-100) exam.

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Objectives

DP-100 - Azure Data Scientist Associate: Machine Learning Classification Models discover the key concepts covered in this course describe the available types of classification models in machine learning describe the steps required to train a classification model describe metrics for determining the best classification model to use use the Azure Machine Learning designer to train a classification model use a subset of the data to train the classification model and run the training pipeline evaluate a classification model by using an evaluate model in Azure Machine Learning Studio use an existing pipeline to create a new inference pipeline to create a predictive service for a classification model deploy a classification model based inference pipeline that can be used by clients summarize the key concepts covered in this course