Unsupervised Algorithms in Machine Learning

One of the most useful areas of machine learning is discovering hidden patterns from unlabeled data. Add the basics of this in-demand skill to your data science toolbox. In this course, we’ll explore select unsupervised learning methods for dimensionality reduction, clustering, and latent feature learning. We’ll also focus on real-world applications like recommender systems, with practical examples of product recommendation algorithms. Prior knowledge of coding or scripting is required. We’ll use Python extensively throughout the course. College-level math skills are required, including Calculus and linear algebra. It’s recommended, but not required, that you take the first course in the specialization, Introduction to Machine Learning: Supervised Learning. This course can be taken for credit toward CU Boulder’s MS in Data Science or MS in Computer Science programs, both offered on Coursera. These fully accredited degrees offer focused courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on three prerequisite courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image by Ryan Wallace on Unsplash.