https://github.com/m0hc3n/machine-learning-algorithms-from-scratch

This repository gathers the essential Machine Learning algorithms coded from scratch using only numpy and sklearn
https://github.com/m0hc3n/machine-learning-algorithms-from-scratch

machine-learning ml ml-algorithms numpy supervised-learning unsupervised-learning

Last synced: about 1 month ago
JSON representation

This repository gathers the essential Machine Learning algorithms coded from scratch using only numpy and sklearn

• Host: GitHub
• URL: https://github.com/m0hc3n/machine-learning-algorithms-from-scratch
• Owner: M0hc3n
• Created: 2023-07-05T17:49:59.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-10-10T08:58:31.000Z (about 1 year ago)
• Last Synced: 2024-10-20T13:23:08.687Z (2 months ago)
• Topics: machine-learning, ml, ml-algorithms, numpy, supervised-learning, unsupervised-learning
• Language: Python
• Homepage:
• Size: 35.2 KB
• Stars: 11
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Support: support_vector_machine/main.py

Awesome Lists containing this project

README

        
# 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: 

```bash

   python -m virtual_env_name /path/to/new/virtual/environment

```

  - Activate the virtual environment:

```bash

   .\virtual_env_name\Scripts\activate

```

  - Install the required libraries:

```bash

   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 \

    


![Recording_2023-07-23_154017_AdobeExpress](https://github.com/Mohcen2311/Machine-Learning-Algorithms-From-Scratch/assets/101293365/c67261cd-eec6-46e6-ac34-bdfe09e1d5c5)

    


    then, you can run:

```bash

   python main.py

```

### Resources:

  - The tutorial that engaged me in creating this repository is [this one](https://www.youtube.com/watch?v=rLOyrWV8gmA), 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](https://towardsdatascience.com/), [Ask Python](https://www.askpython.com/), and [Wikipedia](https://www.wikipedia.org/).

    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](https://www.youtube.com/@statquest), [Visually Explained](https://www.youtube.com/@VisuallyExplained), and [Intuitive Machine Learning](https://www.youtube.com/@IntuitiveMachineLearning).

***Happy Learning!***