Unsupervised learning is really important for analyzing data today. It helps us find patterns and shapes in data that don’t have labels.
Unlike supervised learning, which needs data with labels, unsupervised learning can discover valuable insights even when we don’t know what we’re looking for.
Here are some examples of unsupervised learning:
Clustering: This means putting similar customers together so businesses can market to them better.
Dimensionality reduction: This is about making data simpler while keeping the main features. It’s like cleaning up a messy picture to make it clearer.
These techniques help companies make smarter decisions based on their data!
Unsupervised learning is really important for analyzing data today. It helps us find patterns and shapes in data that don’t have labels.
Unlike supervised learning, which needs data with labels, unsupervised learning can discover valuable insights even when we don’t know what we’re looking for.
Here are some examples of unsupervised learning:
Clustering: This means putting similar customers together so businesses can market to them better.
Dimensionality reduction: This is about making data simpler while keeping the main features. It’s like cleaning up a messy picture to make it clearer.
These techniques help companies make smarter decisions based on their data!