In this video, you will learn how to implement linear regression and apply gradient descent from scratch using numpy in python.
you will also see how we can avoid for loops using vectorization to compute our hypothesis and calculate the required gradients/ partial derivatives to update the learnable parameters/ coefficients.
from the below notebook, hypothesis and gradient functions can be used for linear regression with multiple variables as well.
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Link to Notebook: [ Ссылка ]
