This repository gathers the essential Machine Learning algorithms coded from scratch using only:
- Numpy: for algebraic, and statistical operations
- Sklearn: for generating testing data
- Start by setting up a python virtual environment by running:
python -m virtual_env_name /path/to/new/virtual/environment- Activate the virtual environment:
.\virtual_env_name\Scripts\activate- Install the required libraries:
pip install -r requirements.txt- All the folders contain at least two files:
- model_name.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:
python main.py- 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. I have included all the blogs that I have read to write the code implementation in its corresponding file.
- For people who like to visualize things, I recommend the following youtube channels: StatQuest, Visually Explained, and Intuitive Machine Learning.
Happy Learning!