Registration Deadline: Instructor-led MACHINE LEARNING (Deadline: Jun. 24)
About these Courses
From Electronic Health Records and medical imaging to wearable devices and remote monitoring, technology enables healthcare providers to collect and examine vast amounts of data to gain insights. As data and artificial intelligence (AI) become increasingly pervasive in medicine, it has become critical for health professionals and clinicians to develop a strong understanding of data science and develop the skills to build and evaluate their own machine-learning models.
T-CAIREM is pleased to offer 3 standalone 5-week professional development courses to participants who would like to learn more about the fascinating intersection of data science and medicine. These online, instructor-led courses will provide learners with an introduction to Python, an overview of how to interact with different types of medical data, and case studies on how to apply machine learning on real-world datasets.
Course Delivery
To accommodate learners of different levels, there are 3 sequential courses available. Each course has assignments and takes place over a 5-week window.
All 3 courses will be taught via online, instructor-led lessons using the University of Toronto's Quercus system. Students will be able to reach out and ask questions through Quercus and live office hours with the instructor.
A Certificate of Completion will be issued for each course to participants who complete the required assignments in a timely fashion.
You don't need to take all three courses if you don't want to. Each course is treated as a separate standalone component for learners at different phases of their educational journey.
COURSE 3: Machine Learning
Participants will learn how to train and evaluate machine learning models on real-world datasets in this 5-week course.
Course Dates: July 2 (Wed.) to July 30 (Wed.) from 11:00am to 1:30pm ET
What you'll learn in the Machine Learning course
Course 3 Objectives:
- To learn the terminology associated with machine learning (e.g., train-test splits, cross-validation)
- To learn how to execute an end-to-end machine learning pipeline using the scikit-learn library
- To learn various techniques for inspecting how a machine-learning model makes its decisions
COURSE 3 REGISTRATION DEADLINE: June 24 (Tue.) by 11:30pm ET
