Hands-On Machine Learning with ML.NET: Getting started with Microsoft ML.NET to implement popular machine learning algorithms in C#

• 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

• 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

If 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.