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Offered By: IBMSkillsNetwork

AI Biomedical Applications Workshop

In three fascinating projects, learn how to create biomedical AI applications and deploy them. First, you'll discover the basics of AI and machine learning using Python and Scikit-Learn, building a model to detect Parkinson's disease from voice patterns. Next, you'll dive into deploying a Parkinson's detection app using Docker and Kubernetes, no prior knowledge is needed. Finally, using PyTorch and computer vision techniques, you'll develop an algorithm that identifies metastatic cancer from digital pathology scans. By the end, you'll have the skills to tackle real-world biomedical problems.

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Course

Artificial Intelligence

549 Enrolled
4.6
(67 Reviews)

At a Glance

In three fascinating projects, learn how to create biomedical AI applications and deploy them. First, you'll discover the basics of AI and machine learning using Python and Scikit-Learn, building a model to detect Parkinson's disease from voice patterns. Next, you'll dive into deploying a Parkinson's detection app using Docker and Kubernetes, no prior knowledge is needed. Finally, using PyTorch and computer vision techniques, you'll develop an algorithm that identifies metastatic cancer from digital pathology scans. By the end, you'll have the skills to tackle real-world biomedical problems.

This course covers machine learning in the biomedical field through three projects. You will learn how to build and deploy AI models, including detecting Parkinson's Disease and identifying metastatic cancer. By the end, you'll be equipped with practical skills to apply machine learning to real-world problems and contribute to healthcare innovation. Part 1. parkinson detection; Part 2. deploy the docker container of the parkinson app; Part 3. cancer detection with pytorch.

Course Syllabus

Part 1: Using Machine Learning to Analyze Voice Disorders for Parkinson's Disease Detection
  • Introduction to machine learning and its applications in Biomedicine
  • Understanding voice disorders and Parkinson's disease
  • Implementing different machine learning algorithms such as decision trees and support vector machines
  • Conducting grid search to optimize model parameters
  • Visualizing the models for interpretation and feature identification
  • Building a machine learning model that can accurately predict Parkinson's disease based on voice recordings
Part 2: Deploying AI Application on IBM Code Engine
  • Introduction to IBM Code Engine and its features
  • Understanding serverless platforms and their advantages
  • A step-by-step guide to deploying the AI application on IBM Cloud using IBM Code Engine
  • Using Parkinson's detection model as an example
  • Creating a Docker container image with Kubernetes for app deployment
Part 3: Cancer Detection with PyTorch
  • Introduction to the convolutional neural network and transfer learning
  • Understanding pre-trained CNNs
  • Dataset preparation for PCAM images
  • Training and testing the model
  • Improving model performance using transfer learning
Overall, this course will provide an in-depth understanding of machine learning applications in biomedicine, from detecting Parkinson's disease to identifying metastatic cancer. By the end of the course, students will have the skills to build and deploy AI models and contribute to healthcare innovation.

Estimated Effort

4 Hours

Level

Intermediate

Industries

Healthcare

Skills You Will Learn

Artificial Intelligence, Machine Learning, Data Visualization, Data Analysis, PyTorch

Language

English

Course Code

AI0201EN

Released

April 12, 2023

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