IT Skills     Cloud Computing and Virtualization     Microsoft     DP-100: Designing and Implementing a Data Science Solution on Azure

---

Machine learning clustering models are used to group similar items based on their features and use unsupervised learning. In this course, you'll learn about using clustering models in the Azure Machine Learning Studio.
First, you'll explore the available types of clustering models in Azure Machine Learning Studio and the steps required to train a clustering model. Next, you'll learn how to train and evaluate a clustering model. Next, you'll examine how to create a K-means clustering model in Azure Machine Learning Studio. Finally, you'll learn how to create and deploy a new inference pipeline to create a predictive service for a clustering 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.

---

---

Objectives

DP-100 - Azure Data Scientist Associate: Machine Learning Clustering Models discover the key concepts covered in this course describe the available types of clustering models in machine learning describe the steps required to train a clustering model use the Azure Machine Learning designer to train a clustering model create a K-means clustering model in Azure Machine Learning Studio evaluate a clustering 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 clustering model deploy an clustering model based inference pipeline that can be used by clients summarize the key concepts covered in this course