This course equips professionals with the practical skills to apply machine learning techniques that drive real impact on student success. Using Python and Scikit-learn, participants will learn to prepare higher education datasets, build and refine classification and regression models, and deploy predictive tools that forecast student retention, GPA, and progression.

Through a mix of guided lessons, code briefs, and interactive vibe coding sessions, learners gain experience in data wrangling, feature engineering, model evaluation, and Generative AI applications. Participants design and interpret predictive models to address real-world challenges in higher education, building technical expertise as they begin to compile a portfolio to showcase applied machine learning solutions.

Upon completion of this course, students receive a micro-credential that can be stacked with Course 1 and Course 3 to satisfy requirements for the Applied Analytics and Machine Learning for Higher Education Certificate.