Unsupervised
Learning
Unsupervised learning is a way that computers can learn to find patterns and relationships in data without being told what to look for. It’s called “unsupervised” because there is no one there to supervise the learning process and tell the computer what to do.
For example, let’s say we have a bunch of pictures of animals, and we want the computer to group the pictures by type of animal. We don’t tell the computer what types of animals there are or how many groups to make. Instead, we let the computer figure it out on its own.
To do this, the computer looks at all of the pictures and tries to find patterns and features that are similar. It might notice that some animals have whiskers and others have tails, and it might use these features to create groups of similar animals.
Once the computer has created the groups, we can then look at them and see if they make sense. If the groups are correct, it means that the computer has learned to find patterns and relationships in the data without being told what to look for.
Unsupervised learning is useful for tasks where we don’t have clear examples of what we want the computer to learn, or where we want the computer to discover something new that we don’t already know. It is used in many different applications, such as data mining and image and speech recognition.
Unsupervised learning is a way that computers can learn to find patterns and relationships in data without being told what to look for.
We give the computer a bunch of data and let it figure out patterns and relationships on its own. It looks for features and patterns that are similar, and uses these to group the data into different categories.
It’s called “unsupervised” because there is no one there to supervise the learning process and tell the computer what to do.
Unsupervised learning can be used for many different kinds of tasks, such as data mining and image and speech recognition.
It depends on the task. Supervised learning is good for tasks where we have clear examples of what we want the computer to learn, but unsupervised learning is better for tasks where we don’t have clear examples or we want the computer to discover something new.