Can you learn without a teacher? Computers can, a concept coined unsupervised learning in machine learning literature. For many real world problem, obtaining labels for a dataset can be very expensive, labour intensive and error prone, meaning that following a supervised approach is not practical. And sometimes just imitating human decision making isn’t enough: there may be hidden patterns in the data that add valuable insights.
This course covers the core concepts and algorithms of unsupervised learning. You will gain hands-on experience with applying basic unsupervised machine learning techniques in Python using scikit-learn. You will also learn about algorithms such as k-means, support vector machines, principal component analysis, and DBSCAN.