Supervised Machine Learning With C# And ML.NET
Learn how to build machine learning applications in C# with Microsoft’s new ML.NET library, using feature engineering, regression and classification.
Details
This training course will introduce you to Microsoft’s ML.NET machine learning library. You’ll gain a solid understanding of machine learning and artificial intelligence, including key concepts such as feature engineering, regression and classification.
As you progress through the course material, you’ll design, train, and evaluate sophisticated machine learning models on your computer using C# and ML.NET.
I'll provide you with all required datasets, source code, and libraries to help you get started and build your own machine learning applications with confidence.
Modules
Skills Covered In This Course
Lesson Preview
In this lesson I introduce one of the most common metrics in machine learning: the accuracy. The lesson covers how accuracy can be unreliable when the dataset is biased, the meaning of false positives and false negatives, and how to interpret the confusion matrix in real-world scenarios.
Datasets Used In This Course
Business Case
In 2020, I was invited to Budapest by GLC Europe to deliver a 3-day, in-person version of this very course. The event marked the first time a European training agency offered a training course on Microsoft’s new ML.NET library.
At the time, I had been working closely with Cesar De La Torre, Microsoft’s AI Program Manager, to develop this content and ensure it aligned with Microsoft's vision of the ML.NET library.
This photo was taken right after I finished setting up my laptop and training materials, and just before the students arrived. Over the following three days, my students learned the theory of machine learning and worked through the labs, building their own machine learning apps in C#.
