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Machine Learning

Supervised learning: ML techniques for training models on labeled data, including regression, classification, and clustering

Hi there!

Welcome to the world of supervised learning! In supervised learning, we use machine learning (ML) techniques to train models on labeled data. This means that the data we use to train our models has already been labeled with the correct output, and our goal is to use this labeled data to train a model that can make predictions on new, unseen data.

There are three main types of supervised learning: regression, classification, and clustering.

Regression is used to predict a continuous value, such as a price or a temperature. For example, we might use regression to predict the price of a house based on its size, location, and other features.

Classification is used to predict a discrete value, such as a label or a category. For example, we might use classification to predict whether an email is spam or not spam, or to predict which type of animal is in a photograph.

Clustering is used to group data into clusters or categories based on similarities. For example, we might use clustering to group customers into different segments based on their purchasing habits.

So, in summary, supervised learning is a type of ML that involves training models on labeled data in order to make predictions on new, unseen data. It can be used for a variety of tasks, including regression, classification, and clustering.