Unsupervised Learning ClusteringClustering is one of the most common methods of unsupervised learning. Here, we'll discuss the K-means clustering algorithm.Hierarchical and Density-Based ClusteringWe continue to look at clustering methods. Here, we'll discuss hierarchical clustering and density-based clustering (DBSCAN). Gaussian Mixture Models and Cluster ValidationIn this lesson, we discuss Gaussian mixture model clustering. We then talk about the cluster analysis process and how to validate clustering results.Dimensionality Reduction and PCAOften 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.Random Projection and ICAIn this lesson, we will look at two other methods for feature extraction and dimensionality reduction: Random Projection and Independent Component Analysis (ICA).Project: Identify Customer SegmentsIn this project, you'll apply your unsupervised learning skills to two demographics datasets, to identify segments and clusters in the population, and see how customers of a company map to them.CompanyAbout Us Why Udacity? Blog In the News Jobs at Udacity Become a Mentor Partner with Udacity ResourcesCatalog Career Outcomes Help and FAQ Scholarships Resource Center Udacity SchoolsSchool of Artificial Intelligence School of Autonomous Systems School of Business School of Cloud Computing School of Cybersecurity School of Data Science School of Executive Leadership School of Product Management School of Programming and Development Career Resources Featured ProgramsBusiness Analytics SQL AWS Cloud Architect Data Analyst Intro to Programming Digital Marketing Self Driving Car Engineer Only at UdacityArtificial Intelligence Deep Learning Digital Marketing Flying Car and Autonomous Flight Engineer Intro to Self-Driving Cars Machine Learning Engineer Robotics Software Engineer