Machine learning algorithms
A collection of minimal and clean implementations of machine learning algorithms.
This project is targeting people who wants to learn internals of ml algorithms or implement them from scratch.
The code is much easier to follow than the optimized libraries and easier to play with.
All algorithms are implemented in Python, using numpy, scipy and autograd.
- [Deep learning (MLP, CNN, RNN, LSTM)] (mla/neuralnet)
- [Linear regression, logistic regression] (mla/linear_models.py)
- [Random Forests] (mla/ensemble/random_forest.py)
- [SVM with kernels (Linear, Poly, RBF)] (mla/svm)
- [K-Means] (mla/kmeans.py)
- [PCA] (mla/pca.py)
- [Factorization machines] (mla/fm.py)
- [Gradient Boosting trees (also known as GBDT, GBRT, GBM, XGBoost)] (mla/ensemble/gbm.py)
- Naive bayes
- K-nearest neighbors
git clone https://github.com/rushter/MLAlgorithms cd MLAlgorithms pip install scipy numpy pip install .
How to run examples without installation
cd MLAlgorithms python -m examples/linear_models
Your contributions are always welcome!