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

• 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

• 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

• 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