Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization

Demonstrates how unsupervised learning approaches can be used for dimensionality reduction
Neatly explains algorithms with focus on the fundamentals and underlying mathematical concepts
Describes the comparative study of the algorithms and discusses when and where each algorithm is best suitable for use
Provides use cases, illustrative examples and visualizations of each algorithm
Helps visualize and create compact representations of high dimensional and intricate data for various real-world applications and data analysis