Gradient descent lets models learn by minimizing loss through small, iterative adjustments. Like navigating a foggy hill in the dark, the model takes one step at a time, checking direction to reduce errors steadily.

This optimization method tweaks coefficients and intercepts in models like linear regression. Each adjustment is guided by the learning rate, which determines step size. Too large and the model overshoots; too small and progress stalls.

Feature scaling ensures all input variables contribute equally, avoiding skewed updates. Without it, models struggle to converge efficiently. The process stops when minimum loss is reached, indicating optimal performance.

Next steps involve evaluating model accuracy using metrics like MSE or R-squared. These measures validate whether the training actually improved predictive power beyond random chance.