Machine Learning From Scratch

NumPy implementation of machine learning models from scratch for accessibility. Aim to cover from linear models to neural networks. 🚀

Hopefully you can find the content here helpful in your fantastic ML / DL journey.

Installation

$ git clone https://github.com/kailingding/ML_from_scratch 
$ cd ML_from_scratch 
$ python setup.py install

Table of Content

• Supervised Learning

  • Linear model
  • Logistic model
  • Naive Bayes
  • Decision Tree
  • Random Forest
  • GBDT (WIP)
  • XGBoost (WIP)
  • LightGBM (WIP)

• Unsupervised Learning

  • K-means
  • Gaussian Mixture

• Reinforccement Learning (WIP)

  • Deep Q-Network (WIP)

• Deep Learning (WIP)

  • Activation Function
  • Loss Function
  • Layers (WIP)
    • CNN (WIP)
    • RNN (WIP)
    • LSTM (WIP)
  • Optimizers (WIP)
    • Great resource
    • SGD / Mini-batch GD (WIP)
    • Adagrad (WIP)
    • Adam (WIP)

• Recommendation System (WIP)

  • collaborative Filtering (WIP)
  • Matrix Factorization (WIP)