Module #1 Introduction to Machine Learning in Healthcare Overview of machine learning, its applications in healthcare, and the importance of health predictions
Module #2 Healthcare Data Sources and Types Exploration of healthcare data sources, types, and formats, including EHRs, claims data, and wearables
Module #3 Data Preprocessing for Healthcare Data Techniques for handling missing values, feature scaling, and data transformation in healthcare datasets
Module #4 Introduction to Supervised Learning Fundamentals of supervised learning, including regression and classification, and their applications in healthcare
Module #5 Linear Regression for Healthcare Predictions Application of linear regression to predict continuous health outcomes, such as blood pressure and glucose levels
Module #6 Logistic Regression for Healthcare Predictions Application of logistic regression to predict binary health outcomes, such as disease diagnosis and treatment responses
Module #7 Decision Trees and Random Forests Introduction to decision trees and random forests, and their applications in healthcare prediction models
Module #8 Support Vector Machines (SVMs) for Healthcare Application of SVMs to predict health outcomes, including binary and multi-class classification
Module #9 Introduction to Unsupervised Learning Fundamentals of unsupervised learning, including clustering and dimensionality reduction, and their applications in healthcare
Module #10 K-Means Clustering for Healthcare Data Application of k-means clustering to identify patterns and groups in healthcare data
Module #11 Hierarchical Clustering for Healthcare Data Application of hierarchical clustering to identify patterns and groups in healthcare data
Module #12 Principal Component Analysis (PCA) for Healthcare Data Application of PCA to reduce dimensionality and visualize high-dimensional healthcare data
Module #13 Deep Learning Fundamentals Introduction to deep learning, including neural networks and convolutional neural networks
Module #14 Convolutional Neural Networks (CNNs) for Healthcare Imaging Application of CNNs to analyze medical images, including computer vision and image segmentation
Module #15 Recurrent Neural Networks (RNNs) for Healthcare Time Series Data Application of RNNs to analyze time series healthcare data, including EHRs and vital sign data
Module #16 Model Evaluation and Validation Metrics and techniques for evaluating and validating machine learning models in healthcare
Module #17 Model Interpretability and Explainability Techniques for interpreting and explaining machine learning models in healthcare, including feature importance and SHAP values
Module #18 Ethical Considerations in Machine Learning for Healthcare Discussion of ethical considerations, including bias, fairness, and transparency in machine learning for healthcare
Module #19 Healthcare Predictions with Electronic Health Records (EHRs) Application of machine learning to predict health outcomes using EHR data
Module #20 Predicting Disease Risk and Diagnosis Machine learning models for predicting disease risk and diagnosis, including cardiovascular disease and cancer
Module #21 Predicting Treatment Outcomes and Response Machine learning models for predicting treatment outcomes and response, including personalized medicine
Module #22 Healthcare Predictions with Wearable and IoT Data Application of machine learning to predict health outcomes using wearable and IoT data
Module #23 Machine Learning for Healthcare Policy and Decision-Making Using machine learning to inform healthcare policy and decision-making, including population health management
Module #24 Real-World Applications and Case Studies Real-world applications and case studies of machine learning in healthcare, including success stories and challenges
Module #25 Course Wrap-Up & Conclusion Planning next steps in Machine Learning for Health Predictions career
