100 Facts About Unsupervised Learning

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Here’s a list of 100 facts about unsupervised learning:

1. Unsupervised learning is a type of machine learning where the algorithm is trained on unlabeled data.
2. Clustering and dimensionality reduction are common tasks in unsupervised learning.
3. Unlike supervised learning, unsupervised learning does not require labeled output data for training.
4. The algorithm must find patterns and relationships within the data on its own in unsupervised learning.
5. K-means clustering is a popular algorithm for partitioning data into clusters based on similarity.
6. Hierarchical clustering organizes data into a tree-like structure, representing relationships at different levels.
7. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is an unsupervised clustering algorithm that can identify clusters of varying shapes and sizes.
8. Principal Component Analysis (PCA) is a dimensionality reduction technique used to transform high-dimensional data into a lower-dimensional representation.
9. Autoencoders are neural networks designed for unsupervised learning, learning efficient representations of input data.
10. Generative models, such as Variational Autoencoders (VAEs) and…