Unsupervised Learning

Clustering is one of the most common methods of unsupervised learning. Here, we'll discuss the K-means clustering algorithm.

We continue to look at clustering methods. Here, we'll discuss hierarchical clustering and density-based clustering (DBSCAN).

In this lesson, we discuss Gaussian mixture model clustering. We then talk about the cluster analysis process and how to validate clustering results.

Often we need to reduce a large number of features in our data to a smaller, more relevant set. Principal Component Analysis, or PCA, is a method of feature extraction and dimensionality reduction.

In this lesson, we will look at two other methods for feature extraction and dimensionality reduction: Random Projection and Independent Component Analysis (ICA).