Introduction

In the last article, I discussed what AI is, its uses, types, etc. In this article, I will go into the types of Supervised Learning.

Supervised Learning

Supervised learning is one of the main types of AI (the other main types are unsupervised learning and reinforcement learning).

How it works

Supervised Learning, like any other type of AI, needs lots of good, quality data. What makes it different is that the data needs to be labeled.

For example, if we were making an AI Model to differentiate between an apple and a banana. We need to give the AI Model lots of pictures of apples and label them to make sure the AI knows if the picture we gave it is a Banana or an Apple.

Let me give you a visual:

Classification

Now the example I just gave you is an example of classification. More specifically Binary Classification because there are only two things the model can classify an input as (Banana or Apple).

Let's say we also made the AI Model classify Bananas, Apples, and now Oranges.

This would not be Binary Classification as binary classification only classifies between two things, not more or less than two. If we have more than two things to classify then it's just called Classification.

Regression

Let's look at another type of algorithm called regression. Regression is more focused towards numbers. For example, if you wanted to predict your store's sales for next week based on previous weeks, then that would be regression.

Look at this visual below:

This would be regression. This would be Univariate Regression because we only have one input feature (input) which is sales. The line of best for this data would be linear.

Take a look at the visual below.

If we added another input feature such as if we had an ongoing sale (in this case this input feature is binary. 0 = No sale, 1 = Sale). Then this would become Multivariate Regression.

Conclusion

All in all, Supervised Learning remains one of the most used types of AI algorithms. I hope this knowledge has helped you. Thank you for reading!