Blog: What is ML.NET

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Whenever you think of data science and machine learning, the only two programming languages that pop up on your mind are Python and R. But, Are you a .NET developer? Want to bring machine learning into your applications? Here comes Microsoft recently introduced ML.NET there own machine learning framework specially for .NET developers and C# developers. The framework is open source and cross-platform and can also run on Windows, Linux, and macOS.

Using ML.NET we can perform Machine learning tasks like Classification ( ex: text categorization or sentiment analysis) , regression ( ex : forecasting or price prediction. IT also supports anomaly detection, deep learning, recommendation systems, and natural language processing) etc. Along with some basic algorithms we can even train the model, predict using models along with other basic Machine learning tasks.

We can be extended to work with ML libraries like TensorFlow, Accord.NET, and CNTK etc.

Learning pipelines of ML.NET

  1. Load Data — Its loads Text(CSV/TSV) , Parquet, Binary , fileset into your pipeline
  2. Transform Data — Use the built-in set of transforms to get your data into the format and types that you need for processing ( Example : Text transform, variable encoding, Normalization etc)
  3. Choose Algorithm — Choose the learning algorithm that will provide the highest accuracy for your scenario ( Linear , SVM , K-Means)
  4. Train Model — Train your model by calling the Train method. The method will then return a PredictionModel object that uses your input and output types to make predictions.
  5. Evaluate Model — ML.NET offers evaluators that will assess the performance of your model on a variety of metrics. You can choose the appropriate evaluator depending on your machine learning task.
  6. Deploy Model — ML.NET allows you to save your trained model as a binary file that you can integrate into any .NET application.

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Source: Artificial Intelligence on Medium

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