A walkthrough on how one can implement a linear regression model in Python without using machine learning libraries to gain a more in-depth understanding of how the underlying mathematics work
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With all of the excitement and energy around AI, it is easy to lose sight of all the foundational mathematics and technology that brings it to life. As a professional in the field, you can significantly enhance or renew your understanding of these underlying techniques by coding a model without using machine learning libraries such as sklearn, TensorFlow, PyTorch, and many more.
This inspired me to start a new series called DIY AI. We will dive into various machine-learning models and build them from the ground up. At the end of each article, I aim for the reader to have an extremely in-depth and foundational understanding of the models we build and use daily as data professionals. Let’s get things started with Multiple Linear Regression.
