AI Biomedical Applications Workshop

This workshop is designed for aspiring AI professionals, healthcare researchers, and engineers interested in machine learning in biomedicine. It is suitable for those who want to apply AI to real-world biomedical problems such as disease detection, digital pathology, and precision medicine.

You should have a basic understanding of Python. The workshop introduces Python for biomedical AI and uses frameworks such as Scikit-Learn for healthcare applications, PyTorch, and Docker. No prior knowledge of cloud or deployment tools is required.

Yes. The workshop includes three biomedical machine learning projects:

• Detecting Parkinsons disease from voice recordings.
• Deploying a Parkinsons detection app with Docker and Kubernetes on IBM Code Engine.
• Using deep learning for healthcare to identify metastatic cancer from digital pathology scans.

The course covers AI for disease detection using machine learning algorithms like decision trees and support vector machines. Youll train models on voice disorder datasets and optimize them with grid search for better prediction accuracy.

Voice analysis captures subtle vocal changes linked to Parkinsons disease. By applying biomedical machine learning, the course demonstrates how AI models interpret voice features to support early and accurate diagnosis.

Yes. Youll learn to containerize your Parkinsons detection model, create a Docker image, and deploy it on IBM Code Engine using Kubernetes, giving you hands-on practice with scalable healthcare AI applications.

IBM Code Engine is a fully managed, serverless platform. In this workshop, youll use it to deploy your Parkinsons detection app, gaining practical skills in cloud-based deployment of AI in biomedical science.

PyTorch enables building and training deep neural networks for medical imaging. In this course, youll use convolutional neural networks (CNNs) and transfer learning in biomedicine to identify cancer from digital pathology images.

The cancer detection project uses PCAM (PatchCamelyon) datasets, a standard dataset for real-world biomedical AI applications involving cancer identification from pathology scans.

Yes. Youll explore CNNs and their role in computer vision in healthcare, including feature extraction, image classification, and transfer learning techniques.

Yes. Youll learn how transfer learning in biomedicine leverages pre-trained CNNs to improve cancer detection accuracy while reducing training time.

Absolutely. The workshop emphasizes practicebuilding models, containerizing them with Docker, deploying on IBM Code Engine, and testing AI applications in real-world healthcare scenarios.

No. The course introduces serverless platforms and walks you through every step of cloud deployment, making it beginner-friendly.

By the end, youll gain expertise in:

• Biomedical machine learning model development
• AI in medical imaging using PyTorch
• Deploying AI apps with Docker and Kubernetes
• Using IBM Code Engine for healthcare deployments
• Applying AI in healthcare course concepts to real-world challenges

Yes. You will receive an IBM Certificate, validating your skills in AI in biomedical science and practical biomedical AI applications.

This course prepares you for roles in healthcare AI, biomedical data science, and applied machine learning. With exposure to AI for early disease diagnosis, digital pathology, and deployment skills, youll have a strong foundation for contributing to biomedical innovation.

The projects are designed at an intermediate level, with step-by-step guidance that makes them accessible to beginners familiar with Python.

You will work with Python, Scikit-Learn, PyTorch, Docker, Kubernetes, and IBM Code Engineindustry-relevant tools for AI in biomedical science projects.

The workshop can be completed in a few weeks, depending on your pace. It is structured as a project-based, self-paced program with guided instructions.