Hands-On Machine Learning with ML.NET

Capellman, Jarred

  • 出版商: Packt Publishing
  • 出版日期: 2020-03-27
  • 售價: $1,536
  • 貴賓價: 9.5$1,459
  • 語言: 英文
  • 頁數: 296
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1789801788
  • ISBN-13: 9781789801781
  • 相關分類: .NETMachine Learning 機器學習

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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 Description

Machine 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 for

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.


Jarred Capellman is a Director of Engineering at SparkCognition, a cutting-edge artificial intelligence company located in Austin, Texas. At SparkCognition, he leads the engineering and data science team on the industry-leading machine learning endpoint protection product, DeepArmor, combining his passion for software engineering, cybersecurity, and data science. In his free time, he enjoys contributing to GitHub daily on his various projects and is working on his DSc in cybersecurity, focusing on applying machine learning to solving network threats. He currently lives just outside of Austin, Texas, with his wife, Amy.


  1. Getting started with Machine Learning and ML.NET
  2. Setting up the ML.NET environment
  3. Regression Model
  4. Classification Model
  5. Clustering Model
  6. Anomaly Detection Model
  7. Matrix Factorization Model
  8. Using ML.NET with .NET Core and Forecasting
  9. Using ML.NET with ASP.NET
  10. Using ML.NET with UWP
  11. Training and Building Production Models
  12. Using Tensorflow with ML.NET
  13. Using ONNX with ML.NET