Master the art of game creation with MonoGame-the cross-platform framework of choice for independent developers. Learn the various aspects needed to create your next game by covering MonoGame framework specifics, engine creation, graphics, patterns, and more. The MonoGame framework provides an incredible canvas for the programmer to create their next 2D game, and this book teaches you to make the most of it. You will start from the ground up, beginning with the basics of what MonoGame is, the pipeline, and then how to build a reusable game engine on top of the framework. You will deep dive into various components of each aspect of a game, including graphics, input, audio, and artificial intelligence. The importance of game tooling is also covered. By the end, you will have a mastery level of understanding of how to create a 2D game using MonoGame. With a fully functional 2D game, aspiring developers will have the ideal blueprint to tackle their next fully featured game. The material covered is applicable for almost any 2D game project ranging from side scrolling adventures to fighting games. What You Will Learn Learn to build a game with the MonoGame framework. Understand game engine architecture and how to build an engine onto the MonoGame framework. Grasp common design patterns used in game development and in fully featured engines, such as Unity. Who This Book Is For Beginner to advanced MonoGame programmer would find this book helpful. The audience is expected to have a working knowledge of C#.

Create, train, and evaluate various machine learning models such as regression, classification, and clustering using ML.NET, Entity Framework, and ASP.NET Core Key Features Get well-versed with the ML.NET framework and its components and APIs using practical examples Learn how to build, train, and evaluate popular machine learning algorithms with ML.NET offerings Extend your existing machine learning models by integrating with TensorFlow and other libraries Book DescriptionMachine learning (ML) is widely used in many industries such as science, healthcare, and research and its popularity is only growing. In March 2018, Microsoft introduced ML.NET to help .NET enthusiasts in working with ML. With this book, you'll explore how to build ML.NET applications with the various ML models available using C# code. The book starts by giving you an overview of ML and the types of ML algorithms used, along with covering what ML.NET is and why you need it to build ML apps. You'll then explore the ML.NET framework, its components, and APIs. The book will serve as a practical guide to helping you build smart apps using the ML.NET library. You'll gradually become well versed in how to implement ML algorithms such as regression, classification, and clustering with real-world examples and datasets. Each chapter will cover the practical implementation, showing you how to implement ML within .NET applications. You'll also learn to integrate TensorFlow in ML.NET applications. Later you'll discover how to store the regression model housing price prediction result to the database and display the real-time predicted results from the database on your web application using ASP.NET Core Blazor and SignalR. By the end of this book, you'll have learned how to confidently perform basic to advanced-level machine learning tasks in ML.NET. What you will learn Understand the framework, components, and APIs of ML.NET using C# Develop regression models using ML.NET for employee attrition and file classification Evaluate classification models for sentiment prediction of restaurant reviews Work with clustering models for file type classifications Use anomaly detection to find anomalies in both network traffic and login history Work with ASP.NET Core Blazor to create an ML.NET enabled web application Integrate pre-trained TensorFlow and ONNX models in a WPF ML.NET application for image classification and object detection Who this book is forIf you are a .NET developer who wants to implement machine learning models using ML.NET, then this book is for you. This book will also be beneficial for data scientists and machine learning developers who are looking for effective tools to implement various machine learning algorithms. A basic understanding of C# or .NET is mandatory to grasp the concepts covered in this book effectively.