Unsupervised Learning is a type of machine learning where the program learns the inherent structure of the data based on unlabeled examples.

Clustering is a common unsupervised machine learning approach that finds patterns and structures in unlabeled data by grouping them into clusters.

Some examples:

  • Social networks clustering topics in their news feed
  • Consumer sites clustering users for recommendations
  • Search engines to group similar objects in one cluster

For a quick preview, we will show you an example of unsupervised learning.



NYBD wants to determine how humans and cyborgs differ from each other in terms of:

  • The speed of recovering from wounds
  • Emotional intelligence (EQ)
  • Words per minute (WPM) reading speed

They have taken measurements of these things for the Neo York population and have plotted them on 3 axes.

Press run to see the clusters!

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