Supervised and Unsupervised Machine Learning

Machine learning is a way computers learn from data. There are two main types: supervised learning and unsupervised learning. Let’s break them down!

Supervised Learning

• What It Is: This type uses labeled data. That means each example has both input and the correct output.
• Examples:
  • Classification: Like figuring out if an email is spam or not.
  • Regression: Predicting how much a house might cost.
• Common Methods: Some ways to do this are with linear regression, decision trees, and support vector machines.

Unsupervised Learning

• What It Is: This approach uses data that isn’t labeled. The models look for patterns all by themselves without clear answers.
• Examples:
  • Clustering: Grouping customers based on their behaviors.
  • Dimensionality Reduction: A method to simplify data, like using Principal Component Analysis.
• Common Methods: Some ways to do this are with K-means, hierarchical clustering, and DBSCAN.

In Summary:
Think of supervised learning as having a teacher to help you learn. You get guidance and feedback. On the other hand, unsupervised learning is like exploring on your own. You look for interesting things without anyone telling you what to find.