The Perceptron algorithm, a fundamental concept in machine learning, serves as a building block for more complex models. It was initially proposed by Frank Rosenblatt in the late $1950$s and is widely used for binary classification tasks. The algorithm learns to classify input data points into two categories by adjusting its weights based on errors made during prediction. While relatively simple, the Perceptron algorithm forms the basis for neural networks and other sophisticated machine learning models. Its ability to learn from data and make decisions autonomously makes it a crucial tool in various fields, from pattern recognition to natural language processing.