What is unsupervised learning in machine learning?

One of the challenges with unsupervised learning is that the results can be hard to evaluate. Because there are no labels, it’s not always clear if the clusters or groupings found are “correct.” That’s why interpretation is key.

Unsupervised learning is a type of machine learning where the model is trained on unlabeled data. That means the algorithm tries to find hidden patterns, relationships, or groupings in the dataset without being told the correct answers. A common example is clustering, like grouping customers by purchasing behavior.

Unsupervised learning is very useful in fields like market segmentation, anomaly detection, recommendation systems, and image compression. Since no labels are given, the model must learn the structure on its own.

If you want to explore more, you can check this detailed guide on unsupervised learning: https://www.geeksforgeeks.org/unsupervised-learning/

Think of unsupervised learning as giving the computer a box of puzzle pieces without the picture on the box. The system tries to figure out how things go together. Algorithms like K-means, hierarchical clustering, and principal component analysis (PCA) are widely used.

I’m not a technical expert, but in simple terms, supervised learning is like a teacher telling students the right answers, whereas unsupervised learning is like students trying to figure things out on their own by looking for similarities and differences.