MLAlgorithms Project Overview
The MLAlgorithms project offers a comprehensive set of machine learning algorithms presented in a clear and minimalistic manner. It is an excellent resource for those looking to delve deep into the inner workings of machine learning algorithms or who wish to implement them from scratch.
Purpose of the Project
The primary aim of this project is to cater to individuals who are keen on understanding the inner mechanics of machine learning algorithms without getting bogged down by complex, highly optimized libraries. The straightforward and minimally cluttered codebase allows users to easily follow along and experiment with the algorithms. All the algorithms in this project are written in Python, utilizing well-known packages such as numpy, scipy, and autograd for mathematical computations.
List of Implemented Algorithms
The MLAlgorithms project includes a wide range of machine learning algorithms, each implemented for educational purposes:
- Deep Learning: It includes various neural networks such as Multi-Layer Perceptrons (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory networks (LSTM).
- Regression: Implementations of Linear Regression and Logistic Regression.
- Ensemble Methods: Random Forests and Gradient Boosting Trees (also known as GBDT, GBRT, GBM, and including popular libraries like XGBoost).
- Support Vector Machines (SVM): Includes kernels like Linear, Polynomial, and Radial Basis Function (RBF).
- Clustering: Algorithms like K-Means and Gaussian Mixture Model.
- Neighbor-based Learning: K-nearest neighbors.
- Probabilistic Models: Naive Bayes.
- Dimensionality Reduction: Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE).
- Factorization: Factorization Machines.
- Energy-Based Models: Restricted Boltzmann Machine (RBM).
- Reinforcement Learning: Features Deep Q Learning.
How to Install and Run the Project
Installing and running the MLAlgorithms project is straightforward. It can be done using pip, or alternatively, the examples can be run through Docker for a containerized approach.
Installation Instructions
To install the project, the following steps can be followed:
git clone https://github.com/rushter/MLAlgorithms
cd MLAlgorithms
pip install scipy numpy
python setup.py develop
Running Examples Without Installation
For those who want to try out the examples without full installation, the following can be run:
cd MLAlgorithms
python -m examples.linear_models
Running Examples Within Docker
An alternative is to use Docker for running examples, which involves:
cd MLAlgorithms
docker build -t mlalgorithms .
docker run --rm -it mlalgorithms bash
python -m examples.linear_models
Contribution Opportunities
The MLAlgorithms project welcomes contributions from the community. Whether it's enhancing existing code, improving documentation, or implementing new algorithms, input from users is valued. For substantial changes, contributors are encouraged to open an issue to discuss their proposed modifications.
The MLAlgorithms project is an invaluable educational resource for those looking to gain a deeper understanding of machine learning algorithms through clear and concise Python implementations.