Machine Learning for Computer Scientists

Machine Learning for Computer Scientists provides a comprehensive introduction to foundational machine learning algorithms and their applications, aligned with the AWS Certified Machine Learning Engineer — Associate (MLA-C01). The course covers supervised learning techniques such as linear regression, logistic regression, decision trees, and support vector machines, as well as unsupervised learning methods like clustering and dimensionality reduction. Essential concepts in model evaluation, selection, and optimization are also covered, including cross validation, hyperparameter tuning, and ensemble methods. Designed specifically for computer scientists, this course sets the groundwork for advanced studies in deep learning and natural language processing. This course teaches students machine learning (ML) techniques of supervised and unsupervised ML methods using Python programming language.

Official catalog text — WGU-authored.