So far, the course has been heavily focused on supervised learning algorithms. This week, learn about unsupervised learning algorithms and how they can be applied to clustering and dimensionality reduction problems.
Dimensionality refers to the number of features in the dataset. Theoretically, more features should mean better models, but this is not true in practice. Too many features could result in spurious correlations, more noise, and slower performance. This week, learn algorithms that can be used to achieve a reduction in dimensionality, such as: