Network Science with Python: Explore the networks around us using network science, social network analysis, and machine learning (Paperback)
暫譯: 使用 Python 的網路科學:透過網路科學、社交網路分析與機器學習探索我們周圍的網路

Knickerbocker, David

  • 出版商: Packt Publishing
  • 出版日期: 2023-02-28
  • 售價: $2,040
  • 貴賓價: 9.5$1,938
  • 語言: 英文
  • 頁數: 414
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1801073694
  • ISBN-13: 9781801073691
  • 相關分類: Python程式語言Machine Learning
  • 海外代購書籍(需單獨結帳)

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商品描述

Discover the use of graph networks to develop a new approach to data science using theoretical and practical methods with this expert guide using Python, printed in color

Key Features

• Create networks using data points and information
• Learn to visualize and analyze networks to better understand communities
• Explore the use of network data in both - supervised and unsupervised machine learning projects
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Network analysis is often taught with tiny or toy data sets, leaving you with a limited scope of learning and practical usage. Network Science with Python helps you extract relevant data, draw conclusions and build networks using industry-standard – practical data sets. You’ll begin by learning the basics of natural language processing, network science, and social network analysis, then move on to programmatically building and analyzing networks. You’ll get a hands-on understanding of the data source, data extraction, interaction with it, and drawing insights from it. This is a hands-on book with theory grounding, specific technical, and mathematical details for future reference. As you progress, you’ll learn to construct and clean networks, conduct network analysis, egocentric network analysis, community detection, and use network data with machine learning. You’ll also explore network analysis concepts, from basics to an advanced level.

By the end of the book, you’ll be able to identify network data and use it to extract unconventional insights to comprehend the complex world around you.

What you will learn

• Explore NLP, network science, and social network analysis
• Apply the tech stack used for NLP, network science, and analysis
• Extract insights from NLP and network data
• Generate personalized NLP and network projects
• Authenticate and scrape tweets, connections, the web, and data streams
• Discover the use of network data in machine learning projects

Who this book is for

Network Science with Python demonstrates how programming and social science can be combined to find new insights. Data scientists, NLP engineers, software engineers, social scientists, and data science students will find this book useful. An intermediate level of Python programming is a prerequisite. Readers from both – social science and programming backgrounds will find a new perspective and add a feather to their hat.

商品描述(中文翻譯)

探索使用圖形網絡來開發數據科學的新方法,這本專家指南使用 Python,並以彩色印刷。

主要特點

• 使用數據點和信息創建網絡

• 學習可視化和分析網絡,以更好地理解社群

• 探索在監督式和非監督式機器學習項目中使用網絡數據

• 購買印刷版或 Kindle 書籍包括免費 PDF 電子書

書籍描述

網絡分析通常使用小型或玩具數據集進行教學,這使得學習和實際應用的範圍有限。《使用 Python 的網絡科學》幫助您提取相關數據,得出結論並使用行業標準的實用數據集構建網絡。您將首先學習自然語言處理、網絡科學和社交網絡分析的基本知識,然後進一步編程構建和分析網絡。您將獲得對數據來源、數據提取、與之互動以及從中提取見解的實踐理解。這是一本以理論為基礎的實用書,包含具體的技術和數學細節以供未來參考。隨著進展,您將學會構建和清理網絡、進行網絡分析、自我中心網絡分析、社群檢測,並在機器學習中使用網絡數據。您還將探索網絡分析的概念,從基礎到高級水平。

在書籍結束時,您將能夠識別網絡數據並利用它提取非常規見解,以理解您周圍複雜的世界。

您將學到什麼

• 探索自然語言處理、網絡科學和社交網絡分析

• 應用用於自然語言處理、網絡科學和分析的技術棧

• 從自然語言處理和網絡數據中提取見解

• 生成個性化的自然語言處理和網絡項目

• 驗證和抓取推文、連接、網頁和數據流

• 探索在機器學習項目中使用網絡數據

本書適合誰

《使用 Python 的網絡科學》展示了如何將編程和社會科學結合起來以發現新見解。數據科學家、自然語言處理工程師、軟體工程師、社會科學家和數據科學學生將會發現這本書非常有用。具備中級 Python 編程能力是前提。來自社會科學和編程背景的讀者將會獲得新的視角,為自己的專業增添一筆。

目錄大綱

1. Introducing Natural Language Processing
2. Network Analysis
3. Useful Python Libraries
4. NLP and Network Synergy
5. Even Easier Scraping
6. Graph Construction and Cleaning
7. Whole Network Analysis
8. Egocentric Network Analysis
9. Community Detection
10. Supervised Machine Learning on Network Data
11. Unsupervised Machine Learning on Network Data

目錄大綱(中文翻譯)

1. Introducing Natural Language Processing

2. Network Analysis

3. Useful Python Libraries

4. NLP and Network Synergy

5. Even Easier Scraping

6. Graph Construction and Cleaning

7. Whole Network Analysis

8. Egocentric Network Analysis

9. Community Detection

10. Supervised Machine Learning on Network Data

11. Unsupervised Machine Learning on Network Data