Machine Learning Techniques for Health Diagnostics
( 25 Modules )
Module #1 Introduction to Machine Learning in Healthcare Overview of machine learning applications in healthcare, importance, and challenges
Module #2 Healthcare Data Sources and Types Exploring different sources of healthcare data, including EHR, claims, and wearables
Module #3 Data Preprocessing for Healthcare Analytics Cleaning, transforming, and preparing healthcare data for machine learning
Module #4 Supervised Learning Fundamentals Introduction to supervised learning, including regression, classification, and model evaluation
Module #5 Unsupervised Learning Fundamentals Introduction to unsupervised learning, including clustering, dimensionality reduction, and density estimation
Module #6 Deep Learning Fundamentals Introduction to deep learning, including neural networks, convolutional networks, and recurrent networks
Module #7 Disease Diagnosis using Classification Techniques Applying classification techniques to diagnose diseases from healthcare data
Module #8 Predicting Clinical Outcomes using Regression Techniques Applying regression techniques to predict clinical outcomes from healthcare data
Module #9 Image Analysis for Medical Imaging Diagnosis Applying machine learning to medical imaging data for diagnosis and segmentation
Module #10 Natural Language Processing for Clinical Text Analysis Applying NLP to clinical text data for diagnosis, sentiment analysis, and information extraction
Module #11 Time Series Analysis for Healthcare Data Applying time series analysis techniques to healthcare data for forecasting and anomaly detection
Module #12 Unsupervised Learning for Patient Subtyping Applying unsupervised learning to identify patient subtypes and clusters
Module #13 Deep Learning for Healthcare Image Analysis Applying deep learning techniques to medical imaging data for diagnosis and segmentation
Module #14 Handling Imbalanced Datasets in Healthcare Techniques for handling class imbalance in healthcare datasets
Module #15 Explainability and Interpretability in Healthcare Machine Learning Techniques for explaining and interpreting machine learning models in healthcare
Module #16 Evaluating Machine Learning Models for Healthcare Metrics and techniques for evaluating machine learning models in healthcare
Module #17 Case Studies in Healthcare Machine Learning Real-world examples and case studies of machine learning applications in healthcare
Module #18 Ethical Considerations in Healthcare Machine Learning Ethical considerations and challenges in developing and deploying machine learning models in healthcare
Module #19 Regulatory Considerations in Healthcare Machine Learning Regulatory requirements and challenges in developing and deploying machine learning models in healthcare
Module #20 Data Privacy and Security in Healthcare Machine Learning Ensuring data privacy and security in healthcare machine learning applications
Module #21 Deploying Machine Learning Models in Healthcare Deploying machine learning models in healthcare, including model serving and integration
Module #22 Monitoring and Updating Machine Learning Models in Healthcare Monitoring and updating machine learning models in healthcare, including model drift and concept drift
Module #23 Human-in-the-Loop Machine Learning for Healthcare Integrating human expertise and judgment into machine learning workflows in healthcare
Module #24 Clinical Decision Support Systems using Machine Learning Developing clinical decision support systems using machine learning
Module #25 Course Wrap-Up & Conclusion Planning next steps in Machine Learning Techniques for Health Diagnostics career
