![]() ![]() Inside this file, we state all the columns of our data-set. To do so, we create a file called ModelInput.cs inside a folder called DataModels. Loading in and preprocessing a data-set in ML.NET is quite different from working with other machine learning packages/frameworks because it requires us to state our data structure explicitly. Figure 1: Installing ML.NET Loading in a data-set and creating a data pipeline Depending on your use case you might need to also install some extra packages like Microsoft.ML.ImageAnalytics, Microsoft.ML.TensorFlow or Microsoft.ML.OnnxTransformer. The only thing needed is to install is the Microsoft.ML package. Adding ML.NET to a C# projectĪdding ML.NET to your C# or F# project is fairly straightforward. The complete code for the binary classification model can be found on my Github. In this article, I will show you how to use ML.NET to create a binary classification model, discuss its AutoML capabilities and show you how to use a Tensorflow model with ML.NET. To use the power of Machine Learning in C#, Microsoft created a package called ML.NET, which provides all the basic Machine Learning functionality. One of the most popular languages today is C# which is used for many applications. This can be because of one of many reasons, including already having a code-base in another language or having no experience in Python or R. However, sometimes an individual or company can't or doesn't want to use Python or R. These two languages support every common machine learning algorithm, preprocessing technique, and much more and can be used for almost every machine learning problem. When thinking of data science and machine learning, two programming languages, Python and R, immediately come to mind. ![]()
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