danieljing / models-from-scratch-python

Repo where I recreate some popular machine learning models from scratch in Python

Machine Learning Models from Scratch in Python

A repository where I recreate some popular machine learning models from scratch in Python for educational purposes.
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Table of Contents

1. About The Project
2. Articles
3. Installation

About The Project

This repository is for educational purposes. While the efficiency of the code or completeness is not as good as the one you could find in a library like sklearn, the simplification of the algorithm allows you to really understand how the algorithm works, and why it's being used for a particular dataset.

Every subfolder contains a file demo.ipynb, which contains a practical application of the algorithm. It loads a dataset, perform a basic EDA, and fits the model.

Moreover, I published an article explaining in granular details every model present in this repo. I highly recommend reading them, as I go through on how the model works, the math behind the model, its benefits, assumptions, and cons, and most importantly, a breakdown and explanation of the code to build it.

Articles

• AdaBoost
• Adam
• Batch Normalization
• CART
• Convolutional Neural Networks
• Deep CNN - AlexNet
• ID3 - Decision Tree
• K-Means Clustering
• KNN
• LSTM
• Nadam Optimizer
• Naive Bayes Classifier
• Neural Networks
• Fine-Tuning Deep Neural Networks
• Principal Component Analysis
• Random Forest
• Recurrent Neural Networks
• Stochastic Gradient Descent
• Support Vector Classfier
• XGBoost

Installation

Follow these steps to get a local copy up and running:

1. Clone the repository:

   git clone https://github.com/cristianleoo/models-from-scratch-python.git

2. Install Python packages. You can use pip or conda, depending on your setup:

   pip install -r requirements.txt

3. (Optional) Run the tests to verify that everything is set up correctly:

   python -m unittest discover

Now, you're ready to use the repository!