In Machine Learning, Perceptron is an algorithm for supervised classification of the input into one of several possible non-binary outputs.

The perceptron is a fundamental concept in machine learning, specifically in the realm of supervised learning and neural networks. It is one of the simplest types of artificial neural networks, proposed by Frank Rosenblatt in 1957.

Here’s a concise answer for an interview setting:

“The perceptron is a basic building block of artificial neural networks, inspired by the functioning of neurons in the human brain. It takes multiple input values, each multiplied by a corresponding weight, and sums them up. This sum is then passed through an activation function to produce an output. The perceptron is trained using a supervised learning approach called the perceptron learning rule, where it adjusts its weights based on the error in its predictions compared to the true labels. Perceptrons are often used for binary classification tasks, where they learn to separate input data points into two categories.”

This answer provides a brief overview of what a perceptron is, its components, and its role in machine learning. Depending on the interviewer’s follow-up questions or the depth required for the interview, you may need to elaborate further on topics such as the perceptron learning rule, activation functions, or its limitations compared to more complex neural network architectures.