Multiple Linear Regression using Gradient Descent | Project 2

In this video, we will implement a complete project in one video on Multiple Linear Regression using Gradient Descent Algorithm. We will first understand the Gradient Descent Algorithm in simple words. Although the standard definition is a bit complex: Gradient descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, we use gradient descent to update the parameters of our model.
We will implement the entire code from absolute scratch, reading the dataset, performing gradient descent and then make predictions using Multiple Linear Regression. Towards the end of the video, we will compute some performance metrics that we had discussed in one of the videos like RSE and R-Square Metric to end with the video. The code for the video can be found on GitHub. #python #machinelearning

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Transpose of Matrix:
https://mathinsight.org/matrix_transpose
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?? ?? Course Links:
Complete Code - https://github.com/The-Nerdy-Dev
Visual Studio Code - https://code.visualstudio.com
Git - https://git-scm.com/downloads

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