Azure Data Scientist Associate: Machine Learning Clustering Models

Azure 2021
  • 9 Videos | 57m 36s
  • Earns a Badge
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.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 26s
    UP NEXT
  • Playable
    2. 
    Machine Learning Clustering Models
    9m 39s
  • Locked
    3. 
    Clustering Model Training Concepts
    6m 55s
  • Locked
    4. 
    Clustering Model Training
    11m 19s
  • Locked
    5. 
    Creating a K-means Clustering Model
    7m 49s
  • Locked
    6. 
    Evaluating a Clustering Model
    5m 30s
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    7. 
    Creating a Clustering Model Inference Pipeline
    6m 34s
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    8. 
    Deploying Clustering Model Predictive Services
    7m 42s
  • Locked
    9. 
    Course Summary
    43s

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