Unsupervised Learning

The second part of Machine Learning is :

Unsupervised Learning

An unsupervised model is a type of AI model that learns without any correct answers or labels. This means no one tells the AI what the data means — it has to find patterns, similarities, and groups on its own. The model studies the data carefully and tries to organize it in a way that makes sense. For example, if we show the AI a lot of pictures of animals without saying which one is a cat or a dog, the model will still notice that some animals look alike and group them together. It might make one group of cats and another of dogs — even though it was never told their names! Unsupervised models are useful for discovering hidden patterns in data, such as grouping customers with similar shopping habits or finding topics in a bunch of news articles. It has 2 parts

The first part is :

Clustering

Clustering is when the AI groups similar things together based on how alike they are. The computer doesn’t get any labels or answers — it has to figure out the groups all by itself. For example, if we give the AI a lot of animal pictures without telling it which ones are cats or dogs, it will look at features like shape, size, or color and might group all the cats together in one cluster and all the dogs in another. This happens because the AI notices patterns and similarities in the data. Clustering helps organize large amounts of information so it’s easier to understand. It’s often used in real life — for example, online stores can group customers with similar shopping habits, or scientists can group plants and animals with similar characteristics. In this way, clustering helps the AI discover hidden patterns that humans might not easily see.

The next part is :

Association

Association is when the AI learns to find connections or relationships between different pieces of information. Instead of grouping things, the AI looks for patterns that show how certain items are linked. For example, if people often buy bread and also buy butter, the AI can discover this relationship on its own. It learns that these two items are usually bought together. This kind of learning is very useful in the real world. Online shopping websites use association to recommend products — if you buy a phone, the site might suggest a phone case or charger. Supermarkets also use it to decide which items to place near each other on the shelves. Association helps the AI understand how things are related, so it can make smart predictions and useful suggestions.