Instructor: Ebelechukwu Nwafor, PhD
Part II builds on these basics from Part I, introducing unsupervised learning techniques such as clustering and dimensionality reduction. The module will also cover key concepts like overfitting, and model selection. By the end, students will understand both theoretical and practical aspects of machine learning, with hands-on experience in building and evaluating models using tools like Scikit-Learn.