Multivariate Regression and Gradient Descent

In a previous video, we used linear and logistic regressions as a means of testing the gradient descent algorithm. I was asked to do a video on logistic regression, when I realized the example I used in the gradient descent video was rather complex. Not only is the model more complicated than linear regression, I was using multiple features as well. This would be akin to fitting a plane to 3-d data rather than a line to 2-d in the case of linear regression. In addition there is a lot of matrix manipulation to vectorize the code. In this video, I want to go over that matrix manipulation using the simple case of linear regression and show how by doing this, we can not only get a speed benefit by vectorizing, but also generalize our code to handle multiple parameters with a single gradient function.

Gradient Descent Part 1: https://youtu.be/trvgzYjUr-Y
Gradient Descent Part 2: https://youtu.be/J1ghebX8XGY
Gradient Descent Part 3:https://youtu.be/Twxe59IjHDk

Linear Regression: https://youtu.be/jmKfDvk4k6g
More on Linear Regression: https://youtu.be/UX_b6ZuZLbI

Github: https://github.com/kpmooney/numerical_methods_youtube/blob/master/linear_regression/Multivariate%20Regression.ipynb

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