Algorithm to find gradient descent in Machine learning

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Gradient descent is an optimization algorithm used to minimize the cost function in machine learning by iteratively moving towards the minimum.

Gradient descent calculates the gradient of the cost function with respect to each parameter in the model.
It then updates the parameters in the opposite direction of the gradient to minimize the cost function.
The learning rate determines how big of a step to take in the direction of the gradient.
There are different variations of gradient descent such as batch, stochastic, and mini-batch gradient descent.
Example: In linear regression, gradient descent is used to find the optimal values for the slope and intercept of the line that best fits the data points.

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