IT Blog

Machine Learning

Unsupervised learning: ML techniques for training models on unlabeled data, including clustering and dimensionality reduction

Hey guys, have you ever heard of unsupervised learning? It’s a type of machine learning where we train models on data that isn’t labeled or given any specific outputs. This might sound strange, but it can be really useful in certain situations.

One of the main techniques used in unsupervised learning is called clustering. This is where we try to group data together based on similar characteristics. For example, let’s say we have a bunch of pictures of animals. We might use clustering to group all the pictures of dogs together, all the pictures of cats together, and so on. This way, we can easily see which animals are most similar to each other.

Another technique used in unsupervised learning is dimensionality reduction. This is where we try to reduce the number of variables or “dimensions” in our data. This can be helpful if we have a lot of data and we want to make it easier to analyze.

So why would we want to use unsupervised learning? Well, sometimes we don’t have the resources to label all of our data, or we might not even know what the outputs should be. In these cases, unsupervised learning can help us still get some insights from our data.

Overall, unsupervised learning is a really interesting field of machine learning that can be really useful in certain situations. I hope this helps give you a better understanding of what it is and why it’s useful!