What is the difference between supervised and unsupervised machine learning?

I have been already writing about two main classes of machine learning are unsupervised and supervised. Let’s talk about this in more detail.

Explanation of what is the difference between supervised and unsupervised machine learning.

In supervised machine learning algorithms, we have to provide labelled data, for example, prediction of stock market prices, whereas in unsupervised we need not have labelled data, for example, classification of emails into spam and non-spam.

The difference is that if we feed unlabelled data into a supervised algorithm, it might misclassify some data. For example, if we classify all emails by the content, rather than the sender’s name, we may incorrectly classify a message containing human-readable information as spam.

So, in unsupervised learning algorithms, we have to first identify the features that allow classifying an email as spam and then feed in that data to the algorithm.

Is supervised learning more difficult to implement?

Supervised learning is definitely more difficult to implement as the algorithms used to carry out the calculations are highly complicated. But as the datasets become larger, using unsupervised learning algorithms becomes more efficient because you can efficiently use more data to carry out the same calculation.

In unsupervised learning algorithms, you only need a few samples or training data, and if the data is good enough to detect a pattern, the algorithm will converge to the correct answer.

What are some advantages of unsupervised learning?

On one hand, it is more efficient, meaning that an algorithm can process more data in a shorter period of time.

On the other hand, we do not need to share or transfer labelled data with other people because the unsupervised algorithm can learn by itself. That is why unsupervised learning is essential for AI applications.

Which universities around the world are leading the way?

There are more than a hundred universities around the world that are developing unsupervised learning.

China and the United States are the leading players, and we see that the major companies in the AI space use unsupervised learning algorithms.