Backpropagation is a fundamental algorithm used in training artificial neural networks, enabling them to learn from data. It involves adjusting the weights of the network based on the error of its predictions compared to the actual outcomes. The process consists of two main phases: forward propagation, where inputs are passed through the network to generate outputs, and backward propagation, where the error is calculated and propagated back through the network to update the weights. This iterative process helps improve the model’s accuracy, making backpropagation essential for developing effective machine learning models.