Machine Learning for Biomedical Applications
Learners gain practical skills in applying ML models to medical data and clinical problems.
Instructor
About This Course
Machine Learning for Biomedical Applications provides a practical introduction to machine learning methods used in healthcare and biomedical engineering. The course covers supervised and unsupervised learning, feature extraction, model evaluation, and real-world applications such as disease prediction, medical signal analysis, and clinical decision support. It is designed for biomedical engineers, data scientists, and healthcare professionals seeking to apply AI-driven techniques in medical domains.
Course Outline
Introduction to Machine Learning in Healthcare
Types of Biomedical Data for ML
Data Preprocessing & Feature Engineering
Supervised Learning Algorithms (Regression, Classification)
Unsupervised Learning Techniques (Clustering, Dimensionality Reduction)
Machine Learning for Biomedical Signals
Machine Learning for Medical Imaging (Overview)
Model Training, Validation & Evaluation
Handling Imbalanced & Noisy Medical Data
Ethical, Legal & Clinical Considerations
Case Studies in Biomedical Machine Learning
Mr. Bit
Curriculum Overview
This course includes 0 modules, 0 lessons, and 0 hours of materials.
Course Specifications
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