Machine Learning Frameworks

Course description

Machine learning enables computers to learn patterns from data and make accurate, scalable decisions that power modern technologies across industries. In this course, students are introduced the core workflows of supervised and unsupervised learning and are provided with hands-on experience with modern machine learning frameworks. Students develop end-to-end machine learning pipelines, including data preparation, feature engineering, model training, validation, and performance evaluation using classification and regression techniques. They also apply model selection strategies, address overfitting, and optimize performance using hyperparameter tuning. By the end of the course, students are able to design, evaluate, and refine machine learning models using industry-standard tools and structured development workflows

Students registering for credit courses for the first time must declare a program at the point of registration. Declaring a program does not necessarily mean students must complete a program, individual courses may be taken for skill improvement and upgrading.

For more information, please contact Continuing Education