A collection of minimal and clean implementations of machine learning algorithms.

This project is targeting people who want 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)
• [Support vector machine (SVM) with kernels (Linear, Poly, RBF)] (mla/svm)
• [K-Means] (mla/kmeans.py)
• [Gaussian Mixture Model] (mla/gaussian_mixture.py)
• [K-nearest neighbors] (mla/knn.py)
• [Naive bayes] (mla/naive_bayes.py)
• [Principal component analysis (PCA)] (mla/pca.py)
• [Factorization machines] (mla/fm.py)
• [Restricted Boltzmann machine (RBM)] (mla/rbm.py)
• [t-Distributed Stochastic Neighbor Embedding (t-SNE)] (mla/tsne.py)
• [Gradient Boosting trees (also known as GBDT, GBRT, GBM, XGBoost)] (mla/ensemble/gbm.py)
• [Reinforcement learning (Deep Q learning)] (mla/rl)

 git clone https://github.com/rushter/MLAlgorithms cd MLAlgorithms pip install scipy numpy pip install . 

 cd MLAlgorithms python -m examples.linear_models 

 cd MLAlgorithms docker build -t mlalgorithms . docker run --rm -it mlalgorithms bash python -m examples.linear_models 

Your contributions are always welcome!
Feel free to improve existing code, documentation or implement new algorithm.
Please open an issue to propose your changes if they big are enough.