Machine Learning, Deep Learning, Software Development, Robotics, Linear Regression.
can best fit the historical data. Once we find the best line that fit the data, we can use this regression model for prediction. 2. Compute the gradient. We use the Sum of Squared Errors (SSE) as our loss/ cost function to minimise the prediction error. In this case, the gradient of SSE is a