Accord.NET - Machine learning, Computer vision, Statistics and general scientific computing for .NET

•    CSharp

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The Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile.

machine-learning framework c-sharp nuget visual-studio statistics unity3d neural-network support-vector-machines computer-vision image-processing ffmpeg

Clairvoyant - Software designed to identify and monitor social/historical cues for short term stock movement

•    Python

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Using stock historical data, train a supervised learning algorithm with any combination of financial indicators. Rapidly backtest your model for accuracy and simulate investment portfolio performance.During the testing period, the model signals to buy or sell based on its prediction for price movement the following day. By putting your trading algorithm aside and testing for signal accuracy alone, you can rapidly build and test more reliable models.

machine-learning support-vector-machines portfolio-simulation backtesting-trading-strategies stock-market

svmjs - Support Vector Machine in Javascript (SMO algorithm, supports arbitrary kernels) + GUI demo

•    Javascript

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svmjs is a lightweight implementation of the SMO algorithm to train a binary Support Vector Machine. As this uses the dual formulation, it also supports arbitrary kernels. Correctness test, together with MATLAB reference code are in /test. Corresponding code is inside /demo directory.

support-vector-machines machine-learning classifier svm

fashion - The Fashion-MNIST dataset and machine learning models.

•    R

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Training AI machine learning models on the Fashion MNIST dataset. Fashion-MNIST is a dataset consisting of 70,000 images (60k training and 10k test) of clothing objects, such as shirts, pants, shoes, and more. Each example is a 28x28 grayscale image, associated with a label from 10 classes. The 10 classes are listed below.

mnist fashion dataset fashion-mnist machine-learning artificial-intelligence artificial-neural-networks support-vector-machines svm xgboost data-science r supervised-learning classification image-recognition image-classification

ClassifierToolbox - A MATLAB toolbox for face classifier 1.0.7

•    Matlab

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This package provides various tools for classification, e.g., image classification, face recogntion, and related applicaitons. Run run_me_first for path configurations.

face-recognition classification classification-algorithims covariance-matrix sparse-coding linear-regression linear-discriminant-analysis principal-component-analysis symmetric-positive-definite spd subspace manifold matlab-toolbox dictionary-learning manifold-optimization support-vector-machines src eigenfaces pca

SparseGDLibrary - MATLAB library of gradient descent algorithms for sparse modeling: Version 1.0.2

•    Matlab

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The SparseGDLibrary is a pure-Matlab library of a collection of unconstrained optimization algorithms for sparse modeling. Run run_me_first for path configurations.

optimization optimization-algorithms machine-learning-algorithms machine-learning big-data gradient-descent sparse-linear-solver sparse-regression lasso-regression lasso elasticnet solver algorithms admm proximal-algorithms proximal-operators logistic-regression matrix-completion coordinate-descent support-vector-machines

spampy - Spam filtering module with Machine Learning using SVM (Support Vector Machines).

•    Python

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Spam filtering module with Machine Learning using SVM. spampy is a classifier that uses Support Vector Machines which tries to classify given raw emails if they are spam or not. Support vector machines (SVMs) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier.

machine-learning support-vector-machines sklearn sklearn-classify numpy scipy spam-classification enron-spam-dataset

svm-spam-classifier-javascript - 🍃 Spam Classifier with Data Preparation and Support Vector Machine (SVM)

•    Javascript

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This example project demonstrates how support vector machine (SVM) may be used to solve a classification problem (spam filter) in JavaScript. The SMS Spam Collection Dataset from kaggle is used for the purpose of training and testing the algorithm. Before training the algorithm, the data set is prepared with common practices to finally extract a feature vector for each SMS. Furthermore, svm.js is used for a ready to go SVM implementation. As alternative, uncomment the code to use Naive Bayes classifier instead of SVM from the natural library.

machine-learning machine-learning-algorithms svm support-vector-machine support-vector-machines svm-classifier svm-training svm-learning svm-model svm-library

ETFMachineLearning - Implementation of a paper in q/KDB+ and python - "Forecasting ETFs with Machine Learning Algorithms" - Jim Kyung-Soo Liew and Boris Mayster

•    Jupyter

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Please make sure you use the csv files inside the cvs_files/ directory and point to the right path inside your q code.

quantitative-trading neural-networks deep-learning random-forest support-vector-machines kdb kx

SVR.jl - Support Vector Regression using libSVM

•    Julia

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Support Vector Regression (SVR) analysis in Julia utilizing the libSVM library. SVR is a module of MADS (Model Analysis & Decision Support).

mads julia support-vector-machine regression model-assessment decision-support machine-learning high-performance-computing model-analysis model-reduction support-vector-regression support-vector-machines uncertainty-quantification sensitivity-analysis model-selection model-simulation model-predictions data-modeling data-analytics data-analysis

2018-MachineLearning-Lectures-ESA - Machine Learning Lectures at the European Space Agency (ESA) in 2018

•    Jupyter

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In 2018, The European Space Agency (ESA) organized a series of 6 lectures on Machine Learning at the European Space Operations Centre (ESOC). This repository contains the lectures resources: presentations, notebooks and links to the videos (presentation and hands-on).

machinelearning machine-learning linear-regression support-vector-machines decision-trees random-forest neural-network deep-learning clustering pca anomaly-detection text-mining tf-idf topic-modeling lectures lecture-slides lecture-material lecture-videos