As we know, ML.NET is an open-source and cross-platform (Windows, macOS, Linux) machine learning framework in which you can create custom ML models using C# or F# without having to leave the .NET ecosystem.
ML.NET lets reusing all the knowledge, skills, code, and libraries you already have as a .NET developer so that you can easily integrate machine learning into your web, mobile, desktop, games, and IoT apps.
Moreover, it has been designed as an extensible platform so that you can consume other popular ML frameworks (TensorFlow, ONNX, Infer.NET, and more) and have access to even more machine learning scenarios, like image classification, object detection, and more.
Finally, according to Microsoft's tests, it has high performance and accuracy.
Using a 9GB Amazon review data set, ML.NET trained a sentiment analysis model with 95% accuracy. Other popular machine learning frameworks failed to process the dataset due to memory errors. Training on 10% of the data set, to let all the frameworks complete training, ML.NET demonstrated the highest speed and accuracy.
I will discuss the fundamentals of ML.NET, explore some sample codes, and explain the basics of the Microsoft Machine-Learning framework with a sample code.

This was the start of my journey in C#; it was delightful to code with standard design patterns and make scalable software.  

Design patterns are solutions to software design problems you find again and again in real-world application development. Patterns are about reusable designs and interactions of objects.

Before diving into this article, it is highly recommended to look at Unified Modeling Language cheat sheet.

These are a compilation of publicly shared files of Do-Factory Gang Of Four, O'reilly Head First Design Patterns, Refactoring-Guru dive Into design patterns, and dive Into refactoring books with C# example projects.

It was 2018 that I decided to abandon using Visual-Basic.Net to migrate and use C#, because Microsoft announced tons of new features for this programming language resulting a fast growing community. I decided to release this tutorial which is compiled in 2018.

It is a thorough document that covers most of the capabilities from beginner to advanced including practical examples.

To see the table of contents and download the book, please view the full article.