Easy Guide to Unsupervised Learning
What is It?
Unsupervised learning is a way that machines learn by themselves. They look at data that doesn't have labels or tags. The goal is to find patterns or groups in the data.
What Do We Want to Achieve?
Clustering: This means putting similar pieces of data together. For example, there’s a method called K-means. It helps to divide a set of data into groups by keeping things as similar as possible within each group.
Dimensionality Reduction: This is a fancy way of saying we want to cut down the amount of information but keep the important stuff. One method called PCA helps us keep about 95% of the main information while using fewer features.
Association Rule Learning: This looks for interesting connections between different items. It’s often used in shopping to find out what people tend to buy together.
How is It Used?
People use unsupervised learning for many things, like dividing customers into groups, spotting unusual patterns, and figuring out topics in text. It helps to understand data better, even when we don’t have labels to guide us.
Easy Guide to Unsupervised Learning
What is It?
Unsupervised learning is a way that machines learn by themselves. They look at data that doesn't have labels or tags. The goal is to find patterns or groups in the data.
What Do We Want to Achieve?
Clustering: This means putting similar pieces of data together. For example, there’s a method called K-means. It helps to divide a set of data into groups by keeping things as similar as possible within each group.
Dimensionality Reduction: This is a fancy way of saying we want to cut down the amount of information but keep the important stuff. One method called PCA helps us keep about 95% of the main information while using fewer features.
Association Rule Learning: This looks for interesting connections between different items. It’s often used in shopping to find out what people tend to buy together.
How is It Used?
People use unsupervised learning for many things, like dividing customers into groups, spotting unusual patterns, and figuring out topics in text. It helps to understand data better, even when we don’t have labels to guide us.