Develop Clustering Models with Azure ML Designer

提供方
Microsoft
在此指導項目中,您將:

Create an Azure Machine Learning Workspace using the Azure Portal

Develop a Clustering Model in Azure ML Designer

Publish the model for application use

Clock2 Hours
Intermediate中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

This is an intermediate project on creating clustering models in Azure Machine Learning Studio. Familiarity with any Web Browser and navigating Windows Desktop is assumed. Some background knowledge on Machine Learning or Cloud computing is beneficial but not required to complete this project. Understanding how platform services in the cloud work and how machine learning algorithms function would be of great help in understanding better what we are executing in this guided project. Some minimal data engineering and data scientist knowledge is required. This guided project has the aim to demonstrate how you can create Machine Learning models by using the out-of-the-box solutions that Azure offers, by just using these services as-is, on your own data. The main focus is on the data and how this is being used by the services. As this project is based on Azure technologies, an Azure subscription is required. The project also outlines a step where an Azure subscription will be created and for this, the following items are required: a valid phone number, a credit card, and a GitHub or Microsoft account username. The series of tasks will mainly be carried out using a web browser. If you enjoy this project, we recommend taking the Microsoft Azure AI Fundamentals AI-900 Exam Prep Specialization: https://www.coursera.org/specializations/microsoft-azure-ai-900-ai-fundamentals

您要培養的技能

  • Artificial Intelligence (AI)
  • Machine Learning
  • Cloud Computing
  • clustering

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  1. Create a free trial account in Microsoft Azure and log into Azure using your new subscription.

  2. Create a Resource Group in preparation for creating a new Azure Machine Learning Workspace.

  3. Create an Azure Machine Learning Workspace to manage artifacts related to your machine learning workloads.

  4. Create compute targets on which to run the training process.

  5. Create a dataset and explore data.

  6. Create a pipeline in Azure Machine Learning Designer.

  7. Apply data transformations to cluster observations.

  8. Add training modules and apply a clustering algorithm.

  9. Run the training pipeline to train the model.

  10. Evaluate the clustering model by using the Evaluate Model module.

  11. Create an inference pipeline to assign new data observations.

  12. Publish the predictive service for application use.

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

常見問題

常見問題

還有其他問題嗎?請訪問 學生幫助中心