Optimisation within any machine learning algorithm is incremental to the purity of the algorithm. Gradient Descent Algorithm assists in minimising cost function errors and improving the algorithm’s parameters. Although the Gradient Descent algorithm is used widely in Machine Learning and Deep Learning, its effectiveness can be determined by the quantity of data, amount of iterations and accuracy preferred, and amount of time available. For small-scale datasets, the Batch Gradient Descent is optimal. Stochastic Gradient Descent (SGD) proves to be more efficient for detailed and more extensive data sets. In contrast, Mini Batch Gradient Descent is used for quicker optimisation.