Azure Data Scientist Associate: Machine Learning Classification Models

Azure    |    Intermediate
  • 10 videos | 1h 9m 29s
  • Includes Assessment
  • Earns a Badge
Rating 4.6 of 7 users Rating 4.6 of 7 users (7)
Machine learning classification models are used to predict the class or category that an item belongs to. For example, using patient characteristics such as age, weight, and BMI to predict if they are at risk for specific diseases. In this course, you'll learn about using classification models in the Azure Machine Learning Studio. You'll explore the available types of classification models and the steps required to train a classification model. Next, you'll learn the ideal metrics for determining the best classification model to use for the given data. Finally, you'll examine how to use an existing pipeline to create a new inference pipeline and create and deploy a predictive service for a classification 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 classification models in machine learning
    Describe the steps required to train a classification model
    Describe metrics for determining the best classification model to use
    Use the azure machine learning designer to train a classification model
  • Use a subset of the data to train the classification model and run the training pipeline
    Evaluate a classification 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 classification model
    Deploy a classification model based inference pipeline that can be used by clients
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 33s
  • 8m 12s
  • Locked
    3.  Classification Model Training Concepts
    6m 55s
  • Locked
    4.  Classification Model Selection
    8m 22s
  • Locked
    5.  Training Classification Models
    9m 44s
  • Locked
    6.  Executing the Classification Model Pipeline
    8m 59s
  • Locked
    7.  Evaluating the Classification Model
    5m 26s
  • Locked
    8.  Creating an Inference Pipeline
    10m 50s
  • Locked
    9.  Deploying Classification Model Predictive Services
    8m 45s
  • Locked
    10.  Course Summary
    44s

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