Implementation of several machine learning algorithm from scratch
This repo is a collection of implementations of machine learning algorithms. As a machine learning enthusiast, we often rely heavily on libraries like sklearn, tensorflow, xgboost etc. Although we claim that we understand how they work behind the scene, there is no better way to prove your understanding by implementing it, from scratch.
Many models are easy to implement from an educating perspective. The goal here is to go beyond a 'baby' model, and try to catch up with highly optimized state-of-the-art models.
Currently, this repo includes the following:
- Nearest Neighbour Regressor
- Linear Regressor
- Random Forest Regressor
- Gaussian Process Regressor
I hope to continue this work. Happy learning