Unsupervised learning

1. What’s unsupervised learning?

Unsupervised learning is an approach in machine learning where a model is trained with data sets that are unlabeled, that is, no explicit information about the desired outputs is provided. Rather than seeking to predict specific labels or numerical values, the primary goal of unsupervised learning is to find intrinsic patterns, structures, or relationships in the input data.

2. What’s the unsupervised learning function?

Unsupervised learning, by exploring inherent patterns in unlabeled data sets, offers fundamental utility in revealing hidden structures and relationships. Its ability to identify natural groupings in data using clustering algorithms facilitates the segmentation of information into coherent categories, such as in market analysis and data organization. Furthermore, in dimensionality reduction, unsupervised learning helps simplify complex data sets, preserving key features and facilitating information visualization. This approach finds applications in content recommendation, system anomaly detection, and industrial process improvement by identifying non-obvious patterns. In summary, unsupervised learning powers the discovery of valuable knowledge in unlabeled data, providing an essential tool to explore and understand underlying structure in a variety of contexts.

3. Examples of unsupervised learning?

• K-Means.
• DBSCAN.
• Hierarchical clustering.
• OPTICS.
• MeanShift.
• Restricted Boltzman machines (RBMs).
• Autoencoders.