Predictive Analytics: Detecting Kidney Disease Using AI

Predictive Analytics    |    Intermediate
  • 17 videos | 1h 47m 39s
  • Includes Assessment
  • Earns a Badge
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Nowadays, diseases such as Alzheimer's, heart disease, and diabetes are becoming ever more prevalent across the world. Use this course to get hands-on experience building a pipeline to diagnose chronic kidney disease using Azure Machine Learning designer. Explore the different features of Azure Machine Learning, its interface, and how components and resources come together to build a pipeline. Next, learn how to build a pipeline to create a dataset, implement various data cleaning tasks, and work with the cleaned dataset to build a logistic regression model to detect kidney disease. Finally, examine how models can be trained and evaluated for performance and deploy your pipeline. Upon completion, you'll be able to build and deploy a disease diagnosis Azure Machine Learning pipeline.


  • Discover the key concepts covered in this course
    Recall the features of an azure machine learning workspace
    Create a dataset from a csv file containing kidney disease data
    Study statistics for different fields in a dataset and generate a profile
    Create a simple pipeline that will accept a dataset as input
    Mark specific features in a dataset as containing categorical values
    Apply data cleaning to numeric fields in a dataset
    Handle missing values in categorical fields in a dataset
    Remove duplicate rows from a dataset
  • Group numeric attributes into bins so that they can be treated as categorical fields
    Register cleaned and processed data as a dataset
    Set up an ml pipeline for disease diagnosis
    Split a dataset into train and test sets
    Train a model and view its performance metrics
    Create and use a real-time inferencing pipeline
    Deploy a kidney disease model to an azure container and consume it
    Summarize the key concepts covered in this course


  • 1m 57s
  • 9m 55s
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    3.  Creating a Dataset for an ML Pipeline
    8m 50s
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    4.  Studying the Dataset and Generating a Profile
    8m 3s
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    5.  Building and Running a Pipeline
    8m 33s
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    6.  Marking Fields as Categorical
    11m 6s
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    7.  Cleaning Numeric Data
    5m 24s
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    8.  Handling Missing Categorical Values
    5m 26s
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    9.  Eliminating Duplicate Rows
    5m 6s
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    10.  Grouping Numeric Fields into Bins
    6m 43s
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    11.  Registering a Cleaned Dataset
    2m 5s
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    12.  Building an ML Training Pipeline
    5m 58s
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    13.  Splitting a Dataset into Train and Test Sets
    3m 54s
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    14.  Training and Evaluating a Model
    9m 9s
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    15.  Building a Real-Time Inferencing Pipeline
    6m 5s
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    16.  Deploying a Model for Inferencing
    6m 35s
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    17.  Course Summary
    2m 50s


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