A perceptron is a type of artificial neural network that is used for supervised learning of binary classifiers. It is the simplest type of feedforward neural network, consisting of a single layer of input nodes that are fully connected to a layer of output nodes. The perceptron works as an artificial neuron to perform computations by learning elements and processing them for detecting the business intelligence and capabilities of the input data. The basic components of a perceptron include an input layer, weights, a net sum, and an activation function. The perceptron model begins with multiplying all input values and their weights, then adds these values to create the weighted sum. Further, this weighted sum is applied to the activation function to obtain the desired output. The perceptron is a linear classifier that can learn only linearly separable patterns. It is usually used to classify data into two parts and is also known as a linear binary classifier.