Machine-Learning-Algorithms-From-Scratch

Machine-Learning-Algorithms-From-Scratch This repository gathers the essential Machine Learning algorithms coded from scratch using only: Numpy: for algebraic, and statistical operations Sklearn: for generating testing data Getting Started: Start by setting up a python virtual environment by running: Activate the virtual environment: Install the required libraries: All the folders contain at least two files: - modelname.py: contains the class that implements a specific ML model or technique. - main.py: contains the testing script, it usually has an accuracy check or a plotting of the result. To test the implementation, you can drag and drop the main file to the main directory \ then, you can run: Resources: The tutorial that engaged me in creating this repository is this one, it helps to understand the coding phase of the algorithms, and it contains pretty usefull testing scripts that I have used. Although the previous tutorial was mostly enriching, in the theoretical part, I have taken advantage of insightful blogs written in Towards DataScience, Ask Python, and Wikipedia.