Hands-On Machine Learning with ML.NET (Paperback)

Capellman, Jarred

  • 出版商: Packt Publishing
  • 出版日期: 2020-03-27
  • 售價: $1,920
  • 貴賓價: 9.5$1,824
  • 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
  • 海外代購書籍(需單獨結帳)
    無現貨庫存(No stock available)

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商品描述

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.

商品描述(中文翻譯)

創建、訓練和評估各種機器學習模型,如回歸、分類和聚類,使用ML.NET、Entity Framework和ASP.NET Core

主要特點


  • 通過實際示例熟悉ML.NET框架及其組件和API

  • 學習如何使用ML.NET提供的功能構建、訓練和評估流行的機器學習算法

  • 通過與TensorFlow和其他庫的集成擴展現有的機器學習模型

書籍描述

機器學習(ML)廣泛應用於科學、醫療保健和研究等許多行業,其受歡迎程度不斷增長。2018年3月,微軟推出了ML.NET,以幫助.NET愛好者在處理ML方面更加便捷。通過本書,您將探索如何使用C#代碼構建ML.NET應用程序,並使用各種可用的ML模型。

本書首先概述了ML和使用的各種ML算法類型,並介紹了ML.NET是什麼以及為什麼需要它來構建ML應用程序。然後,您將探索ML.NET框架、其組件和API。本書將作為一本實用指南,幫助您使用ML.NET庫構建智能應用程序。您將逐漸熟悉如何使用實際示例和數據集實現回歸、分類和聚類等ML算法。每個章節都將涵蓋實際實現,向您展示如何在.NET應用程序中實現ML。您還將學習如何在ML.NET應用程序中集成TensorFlow。隨後,您將發現如何將房價預測的回歸模型存儲到數據庫中,並使用ASP.NET Core Blazor和SignalR在網絡應用程序上實時顯示從數據庫中預測的結果。

通過閱讀本書,您將學會在ML.NET中自信地執行從基礎到高級的機器學習任務。

您將學到什麼


  • 使用C#了解ML.NET的框架、組件和API

  • 使用ML.NET為員工流失和文件分類開發回歸模型

  • 評估用於餐廳評論情感預測的分類模型

  • 使用聚類模型進行文件類型分類

  • 使用異常檢測找出網絡流量和登錄歷史中的異常

  • 使用ASP.NET Core Blazor創建支持ML.NET的網絡應用程序

  • 在WPF ML.NET應用程序中集成預訓練的TensorFlow和ONNX模型,用於圖像分類和物體檢測

本書適合人群

如果您是一名希望使用ML.NET實現機器學習模型的.NET開發人員,那麼本書適合您。本書還對於尋找有效工具來實現各種機器學習算法的數據科學家和機器學習開發人員也很有益。理解C#或.NET的基礎知識對於有效掌握本書中涵蓋的概念是必要的。

作者簡介

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.

作者簡介(中文翻譯)

Jarred Capellman 是 SparkCognition 的工程總監,該公司位於德克薩斯州奧斯汀市,是一家尖端的人工智慧公司。在 SparkCognition,他領導著工程和數據科學團隊,開發行業領先的機器學習終端保護產品 DeepArmor,結合了他對軟體工程、網絡安全和數據科學的熱情。在空閒時間,他喜歡每天在 GitHub 上貢獻他的各種項目,並正在攻讀他的網絡安全博士學位,專注於應用機器學習解決網絡威脅。他目前與妻子艾米一起居住在奧斯汀市郊。

目錄大綱

  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

目錄大綱(中文翻譯)

- 開始使用機器學習和ML.NET
- 設置ML.NET環境
- 迴歸模型
- 分類模型
- 聚類模型
- 異常檢測模型
- 矩陣分解模型
- 使用ML.NET與.NET Core和預測
- 使用ML.NET與ASP.NET
- 使用ML.NET與UWP
- 訓練和建立生產模型
- 使用Tensorflow與ML.NET
- 使用ONNX與ML.NET