Golden
Unsupervised learning

Unsupervised learning

A branch of machine learning that tries to make sense of data that has not been labeled, classified, or categorized by extracting features and patterns on its own.

Unsupervised learning is a branch of machine learning that takes unlabeled data that hasn't been previously classified or categorized and tries to extract features and patterns from the data on its own. Where supervised learning is analogous to taking a multiple choice test with pre-determined answer key, unsupervised learning is analogous to taking an open-ended test where the questions don't have an answer key or objective means of determining a grade.



The general goal of unsupervised learning is to gain some insights about a given data set by modeling the underlying structure or distribution in the data. Unsupervised learning algorithms aren't searching for concrete correct answers or specific outputs. Rather, they are handed a dataset without having any explicit instructions on what to do, and they are left alone to find interesting structure in the data. 

Types of Unsupervised Learning

The different unsupervised learning models that exist can be categorized based on the ways in which they organize data.



Timeline

People

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Further reading

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Clustering Based Unsupervised Learning

Syed Sadat Nazrul

Web



Machine Learning for Humans, Part 3: Unsupervised Learning

Vishal Maini

Web



NVIDIA Blog: Supervised Vs. Unsupervised Learning

Isha Salian

Web



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