unsupervised learning

Unsupervised learning is a machine learning technique where algorithms are used to identify patterns and structures in data that has not been labeled or classified by humans. It works without guidance, finding natural groupings, connections, or patterns in the data. This approach is useful when you don’t know what you’re looking for in the data or when labeling data is too expensive or time-consuming. Common tasks in unsupervised learning include clustering (grouping similar data points together), anomaly detection (identifying unusual data points), and dimensionality reduction (simplifying data while preserving its structure)[1][2][3].

Citations:

[1] https://www.ibm.com/topics/unsupervised-learning

[2] https://www.altexsoft.com/blog/unsupervised-machine-learning/

[3] https://en.wikipedia.org/wiki/Unsupervised_learning

[4] https://learn.g2.com/unsupervised-learning

[5] https://www.techtarget.com/searchenterpriseai/definition/unsupervised-learning

[6] https://unsupervised.com/resources/blogs/what-is-unsupervised-learning/

[7] https://www.datacamp.com/blog/introduction-to-unsupervised-learning

[8] https://www.geeksforgeeks.org/supervised-unsupervised-learning/

[9] https://www.javatpoint.com/unsupervised-machine-learning

[10] https://builtin.com/machine-learning/unsupervised-learning