What you will learn

• Apply unsupervised learning methods, such as dimensionality reduction, manifold learning, and density estimation, to transform and visualize data. 

• Understand, evaluate, optimize, and correctly apply clustering algorithms using hierarchical, partitioning, and density-based methods.

• Use topic modeling to find important themes in text data and use word embeddings to analyze patterns in text data. 

• Manage missing data using supervised and unsupervised imputation methods, and use semi-supervised learning to work with partially-labeled datasets.