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.
- 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!