Machine Learning for Healthcare AI (Classroom, Synchronous and Asynchronous e-learning)
About This Course
This course introduces machine learning in healthcare. Learners will explore how to analyse and manage healthcare data, including structured and unstructured types. This course covers essential skills like data collection, cleaning, feature engineering, and model evaluation. Key topics include supervised learning (e.g., regression, classification) and unsupervised learning (e.g., clustering, anomaly detection), with practical applications such as disease prediction and patient segmentation. By mastering these techniques, learners will be ready for roles in healthcare data science, opening doors to careers in analytics and technology-driven healthcare solutions.
What You'll Learn
• Explain the fundamental principles of machine learning and its applications in healthcare, by identifying and classifying structured and unstructured healthcare data and effectively managing data collection, curation, and cleaning for healthcare datasets.
• Apply supervised and unsupervised machine learning algorithms, including regression, classification, clustering, and dimensionality reduction, for healthcare data analysis.
• Evaluate model performance and employ machine learning techniques to address real-world healthcare prediction challenges.
• Use machine learning insights to make data-driven decisions in healthcare scenarios.
• Identify patterns, anomalies, and patient segments within healthcare data to support informed and evidence-based decision-making, contributing to improved patient outcomes and more efficient healthcare processes.
Entry Requirements
The programme is primarily for clinicians and healthcare professionals. Professionals, such as scientists and researchers, interested in how to apply AI technologies to biomedical data are also welcome. There is no pre-requisite to enroll in the programme but some background in the healthcare sector and/or computer literacy would be an advantage.