Predictive Analytics: Identifying Tumors with Deep Learning Models

Predictive Analytics 2022    |    Intermediate
  • 10 Videos | 1h 4m 47s
  • Includes Assessment
  • Earns a Badge
Azure Machine Learning designer allows you to create machine learning models using no-code, drag-and-drop pipelines. Use this course to build pre-trained neural network models that detect diseases from image scans using Azure Machine Learning designer. Learn how to set up data for model training, validation, and testing and how to feed that data into a pipeline that employs a DenseNet model. Next, discover how a model can be configured and substitute a pipeline's DenseNet model with a ResNet model. Finally, explore how a model's training metrics can be analyzed to understand what tweaks need to be applied to build a more reliable model. Upon completion, you'll be able to build DenseNet and ResNet models that can identify tumors from chest scan images.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    set up Azure Storage Explorer and integrate it with an Azure Storage account
    upload images for training, validation, and testing to Azure containers using Azure Storage Explorer
    create datastores and datasets in Azure Machine Learning
    build a pipeline using a template DenseNet model to detect tumor types
  • configure and run an image classification pipeline to detect tumors
    view and analyze the performance metrics of a DenseNet model and configure its parameters
    configure the model's parameters and evaluate the improved performance on the test data
    perform image classification using a ResNet model
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    2m 3s
    UP NEXT
  • Playable
    2. 
    Setting Up Azure Storage Explorer
    8m 56s
  • Locked
    3. 
    Uploading Images to Azure Storage Containers
    5m 35s
  • Locked
    4. 
    Creating Machine Learning Datastores and Datasets
    6m 49s
  • Locked
    5. 
    Building a Pipeline with DenseNet
    8m 49s
  • Locked
    6. 
    Running the Image Classification Pipeline
    9m 42s
  • Locked
    7. 
    Examining Model Performance
    5m 42s
  • Locked
    8. 
    Updating Model Training Parameters
    6m 1s
  • Locked
    9. 
    Performing Image Classification with ResNet
    8m 19s
  • Locked
    10. 
    Course Summary
    2m 52s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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