machinelearning-samples

ML.NET is really a framework which provides ability to build custom traditional machine learning algorithms for a number of scenarios like (classification, regression, recommendation etc.). ML.NET is also extensible, which means we can absorb other leading deep learning frameworks like TensorFlow and Accord.NET through one consistent API providing a uniform way to do ML in .NET. Here's a link to the samples: https://github.com/dotnet/machinelearning-samples

ML.NET is a machine learning framework which is aimed at providing the E2E workflow for infusing ML into .NET apps across pre-processing, feature engineering, modeling, evaluation, and model consumption. The base framework comes with a variety of transforms, trainers, utility apis for loading varies forms of data (e.g. csv's, tsv's) to build custom machine learning models trained with your own data. Using the API you can build a variety of models for scenarios like Sentiment analysis (classification), forecasting, recommendation and also leverage pre-built Tensorflow models for scenarios like image classification. The samples repo provided shows you a variety of scenarios with ML.NET. https://github.com/dotnet/machinelearning-samples @zamalek, would love to learn how we can improve the API to make this stand out :).