Introduction to Machine Learning and Data Analytics

4 credits · Undergraduate

This course introduces students to key techniques in machine learning and data analytics through a diverse set of examples using real datasets from domains such as e-commerce, healthcare, social media, finance, the Internet, and more. Through these examples, conceptual exercises, data analysis exercises in Python, and a comprehensive team project, students will gain experience understanding and applying techniques such as linear regression, logistic regression, classification and regression trees, random forests, boosting, text mining, data cleaning and manipulation, data visualization, time series modeling, clustering, principal component analysis, regularization, and large-scale learning with neural networks.