Basics of Machine Learning

Supervised learning -> The labels already exist for data (Cat/dog image data). You already know whether the image is a cat or a dog. But you give access to labels to models only during the evaluation phase.

Unsupervised learning -> You have no info on whether the image is a dog or a cat in the data. You ask the model to learn based on the features given and group them into categories.

Regression → part of Supervised learning; we try to predict the numerical value. i.e., we try to predict the cat's weight, given the relevant data.

Classification → As part of Supervised learning, we try to predict the categorical value, i.e., expect the color of cat eyes, given the relevant data.