657 Machine Learning

This course provides a comprehensive foundation in machine learning, covering key principles, algorithms, and techniques used to create predictive models. Topics include data collection and selection, exploratory data analysis, data wrangling, visualization and splitting, supervised and unsupervised learning, model training, tuning and evaluation, and neural networks as well as AI ethics. Students will gain hands-on experience with the use of common packages.

Credits

3