ankitasthana

Here is an example of anomaly detection with ML.NET its not online learning based however.

https://blogs.msdn.microsoft.com/dotnet/2018/11/08/announcin...

The base ML.NET framework comes with a variety of numpy alternatives, datatypes for heterogenous (IDataView) and homogenous (VBuffer) data, a variety of transforms for data pre-processing, feature extraction (e.g. OneHotCodeEncoding), Missing values, normalization etc. handy for common ML scenarios like classification, regression, recommendation etc. you can find the full list here. Types:https://github.com/dotnet/machinelearning/blob/master/docs/c... Transforms: https://docs.microsoft.com/en-us/dotnet/machine-learning/res...

The way to think about ML.NET is really a higher level framework which comes built in with traditional ML trainers, transforms etc. and through its extensibility allows .NET developers to also use other leading frameworks for deep learning e.g. TensorFlow and standards e.g. ONNX models using one uniform set of APIs.

You can learn more about ML.NET here: https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotn...

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 :).