Introduction to Machine Learning: Supervised Learning

Introduction to Machine Learning: Supervised Learning offers a clear, practical introduction to how machines learn from labeled data to make predictions and decisions. You’ll build a strong foundation in regression and classification, starting with linear and logistic regression and progressing to resampling, regularization, and tree-based ensemble methods. Along the way, you’ll learn how to evaluate models, manage bias–variance trade-offs, and balance interpretability with predictive power, all while working hands-on in Python. By the end of the course, you’ll have the skills and intuition needed to confidently apply supervised learning techniques to real-world problems. This course can be taken for academic credit as part of CU Boulder’s Masters of Science in Computer Science (MS-CS), Master of Science in Artificial Intelligence (MS-AI), and Master of Science in Data Science (MS-DS) degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Artificial Intelligence: https://www.coursera.org/degrees/ms-artificial-intelligence-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder