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Deep learning (MLP, CNN, RNN, LSTM)https://github.com/awinnie/MLAlgorithms/blob/master/mla/neuralnet
Linear regression, logistic regressionhttps://github.com/awinnie/MLAlgorithms/blob/master/mla/linear_models.py
Random Forestshttps://github.com/awinnie/MLAlgorithms/blob/master/mla/ensemble/random_forest.py
Support vector machine (SVM) with kernels (Linear, Poly, RBF)https://github.com/awinnie/MLAlgorithms/blob/master/mla/svm
K-Meanshttps://github.com/awinnie/MLAlgorithms/blob/master/mla/kmeans.py
Gaussian Mixture Modelhttps://github.com/awinnie/MLAlgorithms/blob/master/mla/gaussian_mixture.py
K-nearest neighborshttps://github.com/awinnie/MLAlgorithms/blob/master/mla/knn.py
Naive bayeshttps://github.com/awinnie/MLAlgorithms/blob/master/mla/naive_bayes.py
Principal component analysis (PCA)https://github.com/awinnie/MLAlgorithms/blob/master/mla/pca.py
Factorization machineshttps://github.com/awinnie/MLAlgorithms/blob/master/mla/fm.py
Restricted Boltzmann machine (RBM)https://github.com/awinnie/MLAlgorithms/blob/master/mla/rbm.py
t-Distributed Stochastic Neighbor Embedding (t-SNE)https://github.com/awinnie/MLAlgorithms/blob/master/mla/tsne.py
Gradient Boosting trees (also known as GBDT, GBRT, GBM, XGBoost)https://github.com/awinnie/MLAlgorithms/blob/master/mla/ensemble/gbm.py
Reinforcement learning (Deep Q learning)https://github.com/awinnie/MLAlgorithms/blob/master/mla/rl
https://github.com/awinnie/MLAlgorithms#installation
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