Azure Data Scientist Associate: Machine Learning Clustering Models
Azure
| Intermediate
- 9 videos | 57m 9s
- Includes Assessment
- 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
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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
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1m 23s
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9m 36s
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3. Clustering Model Training Concepts6m 52s
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4. Clustering Model Training11m 16s
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5. Creating a K-means Clustering Model7m 46s
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6. Evaluating a Clustering Model5m 27s
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7. Creating a Clustering Model Inference Pipeline6m 31s
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8. Deploying Clustering Model Predictive Services7m 39s
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9. Course Summary40s
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