Azure Data Scientist Associate: Machine Learning Regression Models

Azure 2021    |    Intermediate
  • 10 Videos | 1h 16m 7s
  • Includes Assessment
  • Earns a Badge
Machine learning regression models are used to predict numeric labels for the features of an item. In this course, you'll learn more about using regression models in the Azure Machine Learning Studio. First, you'll learn about why regression models are used, the available types of regression models in machine learning, and the steps required to train a regression model. Next, you'll examine the best metrics for determining which regression model to use. You'll learn how to use a subset of data to train the regression model and run the training pipeline. Finally, you'll explore how to use an existing pipeline to create a new inference pipeline and create and deploy a predictive service. 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 what regression models are, why they are used, and the available types of regression models in Azure Machine Learning Studio
    describe the steps required to train a regression model
    describe the best metrics for determining which regression model to use
    use the Azure Machine Learning designer to train a regression model
  • use a subset of data to train the regression model and run the training pipeline
    evaluate a regression 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 regression model
    deploy a regression 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 34s
    UP NEXT
  • Playable
    2. 
    Machine Learning Regression Models
    7m 28s
  • Locked
    3. 
    Regression Model Training Concepts
    7m 31s
  • Locked
    4. 
    Regression Model Selection
    6m 1s
  • Locked
    5. 
    Training a Regression Model
    12m 14s
  • Locked
    6. 
    Executing the Regression Model Pipeline
    9m 1s
  • Locked
    7. 
    Evaluating a Regression Model
    6m 24s
  • Locked
    8. 
    Creating an Inference Pipeline
    11m 53s
  • Locked
    9. 
    Deploying Regression Model Predictive Services
    9m 13s
  • Locked
    10. 
    Course Summary
    47s

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