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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Artificial Intelligence
638 EnrolledAt 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.
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