Machine Learning for Disease Diagnosis and Prevention
( 25 Modules )
Module #1 Introduction to Machine Learning in Healthcare Overview of machine learning applications in healthcare, importance of disease diagnosis and prevention, and course objectives
Module #2 Fundamentals of Machine Learning Basic concepts of machine learning, types of machine learning, and key algorithms
Module #3 Data Preprocessing in Healthcare Importance of data preprocessing, data types, and techniques for handling healthcare data
Module #4 Feature Selection and Engineering Feature selection methods, feature engineering techniques, and dimensionality reduction
Module #5 Supervised Learning for Disease Diagnosis Introduction to supervised learning, regression, and classification algorithms for disease diagnosis
Module #6 Case Study:Diabetes Diagnosis using Supervised Learning Hands-on exercise using supervised learning algorithms for diabetes diagnosis
Module #7 Unsupervised Learning for Disease Subtyping Introduction to unsupervised learning, clustering algorithms, and disease subtyping
Module #8 Case Study:Cancer Subtyping using Unsupervised Learning Hands-on exercise using unsupervised learning algorithms for cancer subtyping
Module #9 Deep Learning for Medical Imaging Analysis Introduction to deep learning, convolutional neural networks (CNNs), and medical imaging analysis
Module #10 Case Study:Breast Cancer Detection using Deep Learning Hands-on exercise using deep learning algorithms for breast cancer detection
Module #11 Natural Language Processing for Clinical Text Analysis Introduction to natural language processing, text preprocessing, and clinical text analysis
Module #12 Case Study:Disease Risk Prediction using Electronic Health Records Hands-on exercise using natural language processing and machine learning for disease risk prediction
Module #13 Machine Learning for Disease Prevention Introduction to machine learning for disease prevention, predictive modeling, and risk factor analysis
Module #14 Case Study:Predicting Cardiovascular Disease using Machine Learning Hands-on exercise using machine learning algorithms for cardiovascular disease prediction
Module #15 Evaluation Metrics for Disease Diagnosis and Prevention Metrics for evaluating machine learning models in disease diagnosis and prevention
Module #16 Model Interpretability and Explainability Techniques for model interpretability and explainability in machine learning for healthcare
Module #17 Ethical Considerations in Machine Learning for Healthcare Ethical considerations, bias, and fairness in machine learning applications in healthcare
Module #18 Regulatory Frameworks for Machine Learning in Healthcare Regulatory frameworks, FDA approval, and compliance for machine learning in healthcare
Module #19 Real-World Applications of Machine Learning in Healthcare Case studies and success stories of machine learning applications in disease diagnosis and prevention
Module #20 Challenges and Limitations of Machine Learning in Healthcare Challenges, limitations, and future directions of machine learning in disease diagnosis and prevention
Module #21 Python for Machine Learning in Healthcare Introduction to Python programming language and popular libraries for machine learning in healthcare
Module #22 R for Machine Learning in Healthcare Introduction to R programming language and popular libraries for machine learning in healthcare
Module #23 Big Data Analytics for Healthcare Introduction to big data analytics, Hadoop, and Spark for healthcare data analysis
Module #24 Cloud Computing for Machine Learning in Healthcare Cloud computing platforms, services, and their applications in machine learning for healthcare
Module #25 Course Wrap-Up & Conclusion Planning next steps in Machine Learning for Disease Diagnosis and Prevention career
