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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
