Unsupervised Learning in Python

Description

In this course, you will learn :

• Discover how to find the underlying groups (or "clusters") in a dataset.
• You'll be clustering companies based on stock market prices and distinguishing different species based on measurement clustering.
• Learn about hierarchical clustering and t-SNE, two unsupervised learning techniques for data visualisation.
• Learn about "Principal Component Analysis," the most fundamental dimension reduction technique ("PCA"). To improve model performance and generalisation, PCA is frequently used prior to supervised learning.
• Learn about "Non-negative matrix factorization" ("NMF"), a dimension reduction technique that expresses samples as combinations of interpretable parts.

Syllabus :

• Clustering for dataset exploration
• Visualization with hierarchical clustering and t-SNE
• Decorrelating your data and dimension reduction
• Discovering interpretable features