This course is tailored to empower you with the knowledge and skills required to harness the potential of machine learning within the medical and health research domains. In this course, you will learn a range of different Artificial Intelligence Algorithms. You will learn the key characteristics of these algorithms, and how to implement them within Python. After completing the course, you will be able to make decisions about when and how to implement different AI solutions for common problems you may encounter.

This course is roughly 20 hours of learnings with video lectures, quizzes and hands-on practical programming examples for you to work through.

This course is for you if:

• You have a foundational understanding of data and wish to explore the world of machine learning as it applies to health research.
• Work in a health-related sector, be it clinical, administrative, or academic roles - this can be as a doctor, health data analyst, public health researcher, and beyond.
• You appreciate a learning structure that adapts to your busy schedule.
• You aim to grasp the core concepts and applications of machine learning in the realm of health, without delving deep into its mathematical intricacies.

Basic working knowledge of Python is required.

Learning objectives of Machine Learning for Health Research.

• Gain an in-depth understanding of diverse ML algorithms and their applications in health research.
• Handle missing data, particularly in the context of Electronic Health Records.
• Utilize and optimize tree-based and ensemble models for complex health data.
• Implement neural networks and understand their learning processes.
• Effectively manage the entire machine learning pipeline from data acquisition to model evaluation.
• Achieve data imputation using sophisticated techniques.
• Understand the ethics and challenges in applying ML in healthcare contexts.