Noise in data refers to random variability or error that is not part of the true signal one wants to learn or measure. In images, noise might be pixel intensity fluctuations due to sensor thermal effects or ISO grain. In audio, noise could be background hiss or hum. In datasets, noise can also mean incorrect labels or outlier points that don’t follow the pattern. Noise can be aleatoric (inherent randomness, like measurement noise) or epistemic (due to lack of knowledge, like mislabeled data). Handling noise is an important aspect: smoothing techniques, regularization, robust losses (like Huber or MAE instead of MSE), and algorithms like RANSAC (which fit models ignoring outliers) are used to mitigate noise. In deep learning, injecting noise (e.g., dropout is like adding noise to activations, or adding noise to inputs) can improve generalization. In signal processing, one often models noise as Gaussian (the “normal noise”) and uses filters (like low-pass filters) to reduce it.