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 UsWhy Udacity?BlogIn the NewsJobs at UdacityBecome a MentorPartner with UdacityResourcesCatalogCareer OutcomesHelp and FAQScholarshipsResource CenterFeatured ProgramsBusiness AnalyticsSQLAWS Cloud ArchitectData AnalystIntro to ProgrammingDigital MarketingSelf Driving Car EngineerOnly at UdacityArtificial IntelligenceDeep LearningDigital MarketingFlying Car and Autonomous Flight EngineerIntro to Self-Driving CarsMachine Learning EngineerRobotics Software EngineerAI Master’sMaster of Business AdministrationMaster of Science in AIUdacity SchoolsSchool of Animation and Game DevelopmentSchool of Artificial IntelligenceSchool of Autonomous SystemsSchool of BusinessCareer ResourcesSchool of Cloud ComputingSchool of CybersecuritySchool of Data ScienceSchool of DevOpsSchool of Executive LeadershipSchool of Product ManagementSchool of Programming and Development