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Gaussian Process Kernels are covariance functions used in Gaussian processes to measure the relationships among data points, providing uncertainty estimates and confidence ratings for forecasts. They are flexible and can handle a wide variety of data patterns, making them useful for predicting trends and non-linear relationships.