Machine Learning for Medical Data

Build your subject-matter expertise

This course is part of the Artificial Intelligence for Healthcare Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

• Learn new concepts from industry experts
• Gain a foundational understanding of a subject or tool
• Develop job-relevant skills with hands-on projects
• Earn a shareable career certificate

You will also translate healthcare data into actionable insights by experimenting with model design, training, and evaluation, strengthening both technical and clinical reasoning skills through practical, outcome-driven projects. Case studies and real-world examples will demonstrate how ML supports disease prediction, treatment optimization, and clinical decision support. The curriculum emphasizes data preprocessing, feature engineering, model selection, and evaluation using clinical metrics and validation strategies. Through hands-on exercises, you will apply supervised and unsupervised methods, design and train neural networks, and address practical challenges such as class imbalance, privacy, and interpretability. You will use Jupyter Notebook files in a Google Colab environment to complete labs. By the end of this course, you will be prepared to implement ML workflows that are clinically relevant, statistically sound, and ethically responsible.