Matt Eland

Build practical C# and .NET 10 projects - from games and web apps to AI chatbots and machine learning tools - while learning modern patterns, libraries, and workflows. Free with your book: DRM-free PDF version + access to Packt's next-gen Reader* Key Features Learn by building practical .NET applications for real-world use Develop cross-platform apps, games, web APIs, and AI integrations Master new C# and .NET features through creative, hands-on projects Book DescriptionToo many C# developers feel stuck building the same apps day in and day out, but learning through side projects can reignite your passion and level up your skills. This book offers a practical, hands-on approach to building confidence with .NET 10 and modern C# by building a variety of engaging applications, from interactive games and productivity tools to machine learning apps and browser-based chatbots. These projects are designed to teach practical patterns and modern tooling, with a focus on learning and experimentation over production hardening. You'll work with tools like Spectre.Console, ML.NET, Uno Platform, and more, developing everything from an adventure game and a card tracker to an AI chatbot. You’ll also build modern AI-enabled systems using Ollama, Microsoft Agent Framework, OpenTelemetry, and Aspire. Written by an experienced C# engineer and teacher, this book blends technical depth with a developer-friendly tone, helping you learn faster and retain more. You'll sharpen your understanding of core .NET capabilities and gain confidence to apply them in your own work or hobby projects. By the end of this book, you'll not only have a portfolio of practical .NET apps - you’ll also have grown as a developer and rediscovered the joy of programming. *Email sign-up and proof of purchase required What you will learn Build an adventure game console app with Spectre.Console Create infinite world simulations using procedural generation Build RESTful APIs using ASP.NET Core and Entity Framework Design a cross-platform chatbot using Uno Platform Use object-oriented programming and unit tests to recreate the Enigma machine Develop browser-based apps with Blazor and JavaScript interop Train and use ML.NET models in your own applications Explore game development with MonoGame and .NET Who this book is forC# developers eager to deepen their practical knowledge of .NET by building meaningful and creative projects. Suitable for professionals and hobbyists alike, this book is ideal for those who enjoy learning through experimentation. Readers should have a basic understanding of C# and familiarity with the .NET ecosystem.

Expand your skillset by learning how to perform data science, machine learning, and generative AI experiments in .NET Interactive notebooks using a variety of languages, including C#, F#, SQL, and PowerShell Key Features Conduct a full range of data science experiments with clear explanations from start to finish Learn key concepts in data analytics, machine learning, and AI and apply them to solve real-world problems Access all of the code online as a notebook and interactive GitHub Codespace Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionAs the fields of data science, machine learning, and artificial intelligence rapidly evolve, .NET developers are eager to leverage their expertise to dive into these exciting domains but are often unsure of how to do so. Data Science in .NET with Polyglot Notebooks is the practical guide you need to seamlessly bring your .NET skills into the world of analytics and AI. With Microsoft’s .NET platform now robustly supporting machine learning and AI tasks, the introduction of tools such as .NET Interactive kernels and Polyglot Notebooks has opened up a world of possibilities for .NET developers. This book empowers you to harness the full potential of these cutting-edge technologies, guiding you through hands-on experiments that illustrate key concepts and principles. Through a series of interactive notebooks, you’ll not only master technical processes but also discover how to integrate these new skills into your current role or pivot to exciting opportunities in the data science field. By the end of the book, you’ll have acquired the necessary knowledge and confidence to apply cutting-edge data science techniques and deliver impactful solutions within the .NET ecosystem.What you will learn Load, analyze, and transform data using DataFrames, data visualization, and descriptive statistics Train machine learning models with ML.NET for classification and regression tasks Customize ML.NET model training pipelines with AutoML, transforms, and model trainers Apply best practices for deploying models and monitoring their performance Connect to generative AI models using Polyglot Notebooks Chain together complex AI tasks with AI orchestration, RAG, and Semantic Kernel Create interactive online documentation with Mermaid charts and GitHub Codespaces Who this book is forThis book is for experienced C# or F# developers who want to transition into data science and machine learning while leveraging their .NET expertise. It’s ideal for those looking to learn ML.NET and Semantic kernel and extend their .NET skills to data science, machine learning, and Generative AI Workflows.

Learn how to improve the maintainability of C# code using modern tools and techniques to resolve technical debt while working safely and efficiently with legacy code Key Features Apply a wide range of refactoring techniques using the latest tools and features of C# Discover ways to safely improve your code using tests, Roslyn analyzers, and AI assistance Find ways of communicating technical debt and improving your code in agile and enterprise settings Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionSoftware projects start as brand-new greenfield projects, but invariably become muddied in technical debt far sooner than you’d expect. In Refactoring with C#, you'll explore what technical debt is and how it arises before walking through the process of safely refactoring C# code using modern tooling in Visual Studio and more recent C# language features using C# 12 and .NET 8. This book, written by a Microsoft MVP, will guide you through the process of refactoring safely through advanced unit testing with XUnit and libraries like Moq, Snapper, and Scientist .NET. You'll explore maintainable code through SOLID principles and defensive coding techniques made possible in newer versions of C#. You'll also find out how to run code analysis and write custom Roslyn analyzers to detect and resolve issues unique to your code. The nature of coding is changing, and you'll explore how to use AI with the GitHub Copilot Chat to refactor, test, document, and generate code before ending with a discussion about communicating technical debt to leadership and getting organizational buy-in to refactor your code in enterprise organizations and in agile teams. By the end of this book, you'll understand the nature of refactoring and see how you can safely, effectively, and repeatably pay down the technical debt in your application while adding value to your business.What you will learn Understand technical debt, its causes and effects, and ways to prevent it Explore different ways of refactoring classes, methods, and lines of code Discover how to write effective unit tests supported by libraries such as Moq Understand SOLID principles and factors that lead to maintainable code Use AI to analyze, improve, and test code with the GitHub Copilot Chat Apply code analysis and custom Roslyn analyzers to ensure that code stays clean Communicate tech debt and code standards successfully in agile teams Who this book is forThis book is for any developer familiar with C# who wants to improve the code they work with on a day-to-day basis. While this book will be most beneficial to new developers with only a year or two of experience, even senior engineers and engineering managers can make the most of this book by exploring not just the process of refactoring, but advanced techniques with libraries like Moq, Snapper, Scientist .NET, and writing custom Roslyn analyzers.