Predictive Analytics in Healthcare Competency (Intermediate Level)

  • 16m
  • 16 questions
The Predictive Analytics in Healthcare Competency (Intermediate Level) benchmark measures your ability to identify and apply predictive analytics in the healthcare domain. You will be evaluated on your skills in using predictive analytics for various use cases, such as detecting kidney diseases and identifying tumors with deep learning models. A learner who scores high on this benchmark demonstrates that they have experience performing predictive analytics on data in the healthcare domain.

Topics covered

  • apply data cleaning to numeric fields in a dataset
  • build a pipeline using a template DenseNet model to detect tumor types
  • configure and run an image classification pipeline to detect tumors
  • configure the model's parameters and evaluate the improved performance on the test data
  • create and use a real-time inferencing pipeline
  • create a simple pipeline that will accept a dataset as input
  • create datastores and datasets in Azure Machine Learning
  • deploy a kidney disease model to an Azure container and consume it
  • group numeric attributes into bins so that they can be treated as categorical fields
  • perform image classification using a ResNet model
  • register cleaned and processed data as a dataset
  • set up an ML pipeline for disease diagnosis
  • set up Azure Storage Explorer and integrate it with an Azure Storage account
  • study statistics for different fields in a dataset and generate a profile
  • upload images for training, validation, and testing to Azure containers using Azure Storage Explorer
  • view and analyze the performance metrics of a DenseNet model and configure its parameters