In machine learning, a weight represents the strength of the connection between two nodes in a network. It is a numerical value that adjusts the input's significance, essentially meaning how much influence it has on the prediction outcome. For example, in a neural network model, adjusting the weights during training can either amplify or reduce the data's impact, improving the model's performance.