This module focuses on applying supervised learning algorithms to healthcare datasets and clinical prediction tasks. Learners acquire the skills to select and implement appropriate supervised learning methods for disease risk prediction and treatment outcome modeling. Key areas include preprocessing clinical data, handling missing values, and engineering meaningful medical features to improve model accuracy and interpretability. The curriculum addresses class imbalance challenges common in healthcare through techniques like SMOTE, cost-sensitive learning, and appropriate evaluation metrics beyond accuracy. Through hands-on labs, students build practical models including diabetes risk predictors, clean real-world clinical datasets, and develop rare condition detectors using precision-recall evaluation methods for clinical applications.

This module teaches unsupervised learning techniques for discovering hidden patterns in medical data without labeled outcomes. Students learn to apply clustering algorithms like K-means, hierarchical clustering, and DBSCAN for patient segmentation and personalized care strategies. The curriculum covers dimensionality reduction methods, including PCA, t-SNE, and UMAP, for simplifying high-dimensional healthcare datasets while preserving essential information. Key focus areas include interpreting unsupervised results for clinical relevance and translating abstract clusters into actionable treatment decisions. Through practical labs, students perform patient clustering analysis, visualize genomic data in reduced dimensions, and develop workflows for integrating unsupervised learning outcomes into electronic health record systems for real-world clinical applications.

This module covers neural network applications for healthcare datasets, focusing on deep learning architectures tailored for medical contexts. Students learn to design and train neural networks for clinical prediction tasks, applying convolutional neural networks (CNNs) for medical imaging analysis and recurrent neural networks (RNNs) for sequential clinical data. The curriculum includes advanced CNN architectures like ResNet and DenseNet for radiology applications, plus LSTM networks for modeling patient timelines and predicting clinical deterioration. A key emphasis is placed on explainability methods, including saliency maps and Grad-CAM, to provide transparency in deep learning medical predictions. Through hands-on labs, students build disease detection systems, ICU risk prediction models, and implement interpretability techniques for clinical decision support.

This capstone project consolidates the knowledge gained throughout the course and guides learners through a comprehensive, hands-on application of Machine Learning in healthcare. Learners will revisit key concepts while developing predictive models for disease detection using electronic health records and medical imaging data. Students engage in case-based problem-solving, implementing algorithms like neural networks while addressing healthcare-specific challenges, including data privacy, class imbalance, and model interpretability. Emphasis is placed on real-world clinical relevance, ethical AI practice, and professional readiness. Through validation testing, ethical analysis, and implementation recommendations, this capstone experience reinforces both conceptual mastery and practical competence in healthcare ML applications.