Book Image

C# 8.0 and .NET Core 3.0 – Modern Cross-Platform Development - Fourth Edition

By : Mark J. Price
Book Image

C# 8.0 and .NET Core 3.0 – Modern Cross-Platform Development - Fourth Edition

By: Mark J. Price

Overview of this book

In C# 8.0 and .NET Core 3.0 – Modern Cross-Platform Development, Fourth Edition, expert teacher Mark J. Price gives you everything you need to start programming C# applications. This latest edition uses the popular Visual Studio Code editor to work across all major operating systems. It is fully updated and expanded with new chapters on Content Management Systems (CMS) and machine learning with ML.NET. The book covers all the topics you need. Part 1 teaches the fundamentals of C#, including object-oriented programming, and new C# 8.0 features such as nullable reference types, simplified switch pattern matching, and default interface methods. Part 2 covers the .NET Standard APIs, such as managing and querying data, monitoring and improving performance, working with the filesystem, async streams, serialization, and encryption. Part 3 provides examples of cross-platform applications you can build and deploy, such as web apps using ASP.NET Core or mobile apps using Xamarin.Forms. The book introduces three technologies for building Windows desktop applications including Windows Forms, Windows Presentation Foundation (WPF), and Universal Windows Platform (UWP) apps, as well as web applications, web services, and mobile apps.
Table of Contents (21 chapters)

Understanding machine learning

Marketing folk love to use terms like artificial intelligence or data science in their promotional materials. Machine learning is a subset of data science. It is one practical way to add intelligence to software.

More Information: You can learn the science behind one of the most popular and successful data science techniques by enrolling in Harvard University's free Data Science: Machine Learning 8-week course at the following link:

This book cannot teach machine learning in one chapter. If you need to understand how machine learning algorithms work internally then you would need to understand data science topics including calculus, statistics, probability theory, and linear algebra. Then you would need to learn about machine learning in depth.

More Information: To learn about machine learning in depth, read Python Machine Learning by Sebastian Raschka...