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README.en.md

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+# Machine learning algorithms
+A collection of minimal and clean implementations of machine learning algorithms.
+
+### Why?
+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.  
+
+### Implemented:
+* [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)
+
+
+### Installation
+```sh
+        git clone https://github.com/rushter/MLAlgorithms
+        cd MLAlgorithms
+        pip install scipy numpy
+        python setup.py develop
+```
+### How to run examples without installation
+```sh
+        cd MLAlgorithms
+        python -m examples.linear_models
+```
+### How to run examples within Docker
+```sh
+        cd MLAlgorithms
+        docker build -t mlalgorithms .
+        docker run --rm -it mlalgorithms bash
+        python -m examples.linear_models
+```
+### Contributing
+
+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 are big enough.