A simple machine learning library featuring proof-of-concept implementations of fundamental statistics and machine learning algorithms in Python and R. This project serves the sole purpose of aiding my own learning of the subject.
- Neural networks (Jupyter Notebook demo)
- Decision Trees, Random Forests and Gradient Boosted Trees (Jupyter Notebook demo)
- Sequential Importance Resampling and Resample Move Particle Filter (R Markdown demo)
- Kernel Ridge Regression and Kernel Smoothing (Interactive Shiny web app)
- Collaborative Filtering (Jupyter Notebook demo)
- K-Means Clustering (Jupyter Notebook demo)
The project is organised into different folders. Each folder contains the implementation of a different algorithm alongside a Jupyter notebook,R Markdown document or Shiny web app to demonstrate the use of the algorithm. The links in the list above take you directly to the demos.