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K
Lectures
Course Information
Lecture 1: K-Means Clustering
Lecture 2: DBSCAN and Hierarchical Clustering
Lecture 3: Introduction to Principal Component Analysis (PCA)
Lecture 4: More PCA, LSA, and NMF
Lecture 5: Word Embeddings, word2vec
Lecture 6: Using word embeddings, manifold learning
Lecture 7: Recommender Systems Part I
Lecture 8: Recommender Systems Part 2
Appendices
Appendix A: K-Means customer segmentation case study
Class demos
Lecture 01: Clustering class demo
Lecture 02: Clustering class demo
Lecture 03: PCA applications class demo
Lecture 07: Collaborative filtering class demo
Attribution
Attributions
Index
