A Learning Rate Schedule is a strategy used in machine learning to adjust the learning rate during training. The learning rate determines how much to change the model's parameters in response to the estimated error each time the model weights are updated. By modifying the learning rate over time, it can help improve the model's performance and convergence speed. Common types of learning rate schedules include Step Decay, where the learning rate decreases by a factor at specific intervals, and Exponential Decay, which reduces the learning rate continuously. These schedules help prevent overshooting the optimal solution and can lead to better training outcomes.