C# Machine Learning Projects: Nine real-world projects to build robust and high-performing machine learning models with C#

Yoon Hyup Hwang

  • 出版商: Packt Publishing
  • 出版日期: 2018-06-14
  • 售價: $1,520
  • 貴賓價: 9.5$1,444
  • 語言: 英文
  • 頁數: 350
  • 裝訂: Paperback
  • ISBN: 1788996402
  • ISBN-13: 9781788996402
  • 相關分類: C#Machine Learning
  • 立即出貨 (庫存=1)

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商品描述

Power your C# and .NET applications with exciting machine learning models and modular projects

Key Features

  • Produce classification, regression, association, and clustering models
  • Expand your understanding of machine learning and C#
  • Get to grips with C# packages such as Accord.net, LiveCharts, and Deedle

Book Description

Machine learning is applied in almost all kinds of real-world surroundings and industries, right from medicine to advertising; from finance to scientifc research. This book will help you learn how to choose a model for your problem, how to evaluate the performance of your models, and how you can use C# to build machine learning models for your future projects.

You will get an overview of the machine learning systems and how you, as a C# and .NET developer, can apply your existing knowledge to the wide gamut of intelligent applications, all through a project-based approach. You will start by setting up your C# environment for machine learning with the required packages, Accord.NET, LiveCharts, and Deedle. We will then take you right from building classifcation models for spam email fltering and applying NLP techniques to Twitter sentiment analysis, to time-series and regression analysis for forecasting foreign exchange rates and house prices, as well as drawing insights on customer segments in e-commerce. You will then build a recommendation model for music genre recommendation and an image recognition model for handwritten digits. Lastly, you will learn how to detect anomalies in network and credit card transaction data for cyber attack and credit card fraud detections.

By the end of this book, you will be putting your skills in practice and implementing your machine learning knowledge in real projects.

What you will learn

  • Set up the C# environment for machine learning with required packages
  • Build classification models for spam email filtering
  • Get to grips with feature engineering using NLP techniques for Twitter sentiment analysis
  • Forecast foreign exchange rates using continuous and time-series data
  • Make a recommendation model for music genre recommendation
  • Familiarize yourself with munging image data and Neural Network models for handwritten-digit recognition
  • Use Principal Component Analysis (PCA) for cyber attack detection
  • One-Class Support Vector Machine for credit card fraud detection

Who This Book Is For

If you're a C# or .NET developer with good knowledge of C#, then this book is perfect for you to get Machine Learning into your projects and make smarter applications.

Table of Contents

  1. Basics of machine learning modeling
  2. Spam email filtering
  3. Twitter sentiment analysis
  4. Foreign exchange rate forecast
  5. Fair value of house/property
  6. Customer segmentation
  7. Music genre recommendation
  8. Handwritten digit recognition
  9. Cyber attack detection
  10. Credit card fraud detection
  11. What is next?

商品描述(中文翻譯)

使用令人興奮的機器學習模型和模組化專案來強化您的C#和.NET應用程式

主要特點:
- 創建分類、回歸、關聯和分群模型
- 擴展您對機器學習和C#的理解
- 掌握C#套件,如Accord.net、LiveCharts和Deedle

書籍描述:
機器學習幾乎應用於各種現實世界的環境和行業,從醫學到廣告,從金融到科學研究。本書將幫助您學習如何為您的問題選擇模型,評估模型的性能,以及如何使用C#為未來的專案建立機器學習模型。

通過基於專案的方法,您將獲得機器學習系統的概述,以及作為C#和.NET開發人員,如何將您現有的知識應用於各種智能應用。您將首先設置C#環境,並使用所需的套件Accord.NET、LiveCharts和Deedle進行機器學習。然後,我們將從建立用於垃圾郵件過濾的分類模型,應用NLP技術進行Twitter情感分析,到使用時間序列和回歸分析外匯匯率和房價預測,以及在電子商務中繪製客戶分段的見解。然後,您將建立一個音樂類型推薦模型和一個手寫數字的圖像識別模型。最後,您將學習如何檢測網絡和信用卡交易數據中的異常,以進行網絡攻擊和信用卡欺詐檢測。

通過閱讀本書,您將能夠將您的技能應用於實際專案中,並實現您的機器學習知識。

您將學到什麼:
- 設置C#環境,使用所需的套件進行機器學習
- 建立用於垃圾郵件過濾的分類模型
- 掌握使用NLP技術進行Twitter情感分析的特徵工程
- 使用連續和時間序列數據預測外匯匯率
- 創建音樂類型推薦模型
- 熟悉處理圖像數據和用於手寫數字識別的神經網絡模型
- 使用主成分分析(PCA)進行網絡攻擊檢測
- 使用單類支持向量機進行信用卡欺詐檢測

本書適合對C#或.NET有良好知識的開發人員,讓您將機器學習應用於您的專案中,創建更智能的應用程式。

目錄:
1. 機器學習建模基礎
2. 垃圾郵件過濾
3. Twitter情感分析
4. 外匯匯率預測
5. 房屋價值估算
6. 客戶分段
7. 音樂類型推薦
8. 手寫數字識別
9. 網絡攻擊檢測
10. 信用卡欺詐檢測
11. 下一步該做什麼?