A Perceptron is a type of artificial neuron used in machine learning, particularly in the field of neural networks. It serves as a basic building block for more complex models. The Perceptron takes multiple input values, applies weights to them, and produces a single output based on a threshold function. This allows it to classify data into two categories. Developed by Frank Rosenblatt in the late 1950s, the Perceptron is designed to mimic the way biological neurons work. It learns from training data by adjusting its weights through a process called gradient descent, enabling it to improve its classification accuracy over time.