A student is exploring how to use gradient descent to fit a straight line to a small dataset. The dataset shows years of experience versus salary (in thousands), and the goal is to find the best-fitting line using an iterative update rule.

Your task is to adjust the slope ($m$**) and intercept ($b$) so that the line closely follows the data points.

The expression you are trying to minimize is:

The gradient descent update rules for minimizing this function are:

Where:

• $\alpha$ is the learning rate (step size);
• $\frac{\partial J}{\partial m}$ is the partial derivative of the loss function with respect to $m$;
• $\frac{\partial J}{\partial b}$ is the partial derivative of the loss function with respect to $b$.

This loss measures how far off your predicted points are from the actual data. (P.S. Smaller values mean the line fits the data better.)

In order to find values $m$ and $b$, use gradient descent.