From the course: Exploring Data Science with .NET using Polyglot Notebooks & ML.NET

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Additional ML.NET topics

Additional ML.NET topics

From the course: Exploring Data Science with .NET using Polyglot Notebooks & ML.NET

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Additional ML.NET topics

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- [Instructor] We've now seen how to use ML.NET and AutoML to execute simple machine learning experiments, which was our objective with this course as an introduction to data science for .NET developers. However, I also think it's important for you to understand how much more there is to ML.NET than what we've covered here. So here are some areas that you can learn and grow in beyond this course. First, ML.NET supports a wide range of tasks. If you wanted to try out more of ML.NET, I recommend picking a task of interest and investigating how to perform it using AutoML. Secondly, our experiments in this module involved a test set and a training set, but ML.NET also supports cross-validation. Cross-validation involves dividing your data into multiple folds, and then repeating the same experiment with each different fold of training and testing data. This helps reduce the impact of randomization when splitting your test and training datasets. You should also know that under the hood…

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