Introduction to Supervised Learning ¶ the task of supervised learning is as follows: Given a training set of N example input-output pairs (x1,y1), (x2,y2), … (xN,yN) (x 1, y 1), (x 2, y 2), (x N, y N) ...

Some unsupervised algorithms have the ability to pre-process data, uncovering clusters and associations that serve as valuable inputs for a supervised model, thereby enhancing its forecasting ...

This week, I debated with my friend whether one should consider that Generative AI tools are created through supervised or unsupervised learning. At the end of it, I lost the debate.

Machine learning can be supervised, unsupervised, or semi-supervised. In supervised learning, models are trained on labeled data, meaning the input data is paired with the correct output.