Predictive Analytics: Detecting Kidney Disease Using AI

Predictive Analytics    |    Intermediate
  • 17 videos | 1h 47m 39s
  • Includes Assessment
  • Earns a Badge
Rating 4.7 of 24 users Rating 4.7 of 24 users (24)
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.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

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

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.

Digital badges are yours to keep, forever.

YOU MIGHT ALSO LIKE

Course Predictive Analytics: Case Studies on Predictive Analytics for Healthcare
Rating 4.4 of 53 users Rating 4.4 of 53 users (53)
Course Fundamentals of AI & ML: Advanced Data Science Methods
Rating 4.4 of 136 users Rating 4.4 of 136 users (136)
Course Predictive Analytics: Case Studies for Operations
Rating 4.3 of 23 users Rating 4.3 of 23 users (23)

PEOPLE WHO VIEWED THIS ALSO VIEWED THESE

Course Getting started in Excel 365
Rating 4.4 of 6009 users Rating 4.4 of 6009 users (6009)
Course Supervised, Unsupervised & Deep Learning
Rating 4.5 of 258 users Rating 4.5 of 258 users (258)
Course Google Chart Tools: Basic Charts
Rating 4.5 of 73 users Rating 4.5 of 73 users (73)