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Network science with Python : explore the networks around us using network science, social network analysis, and machine learning /
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 commu...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing Limited,
2023.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Knickerbocker, David, |e author. | |
| 245 | 1 | 0 | |a Network science with Python : |b explore the networks around us using network science, social network analysis, and machine learning / |c David Knickerbocker. |
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| 520 | |a 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. | ||
| 505 | 0 | |a Cover -- Title Page -- Copyright and Credits -- Acknowledgements -- Contributors -- Table of Contents -- Preface -- Part 1: Getting Started with Natural Language Processing and Networks -- Chapter 1: Introducing Natural Language Processing -- Technical requirements -- What is NLP? -- Why NLP in a network analysis book? -- A very brief history of NLP -- How has NLP helped me? -- Simple text analysis -- Community sentiment analysis -- Answer previously unanswerable questions -- Safety and security -- Common uses for NLP -- True/False -- Presence/Absence -- Regular expressions (regex) | |
| 505 | 8 | |a Word counts -- Sentiment analysis -- Information extraction -- Community detection -- Clustering -- Advanced uses of NLP -- Chatbots and conversational agents -- Language modeling -- Text summarization -- Topic discovery and modeling -- Text-to-speech and speech-to-text conversion -- MT -- Personal assistants -- How can a beginner get started with NLP? -- Start with a simple idea -- Accounts that post most frequently -- Accounts mentioned most frequently -- Top 10 data science hashtags -- Additional questions or action items from simple analysis -- Summary -- Chapter 2: Network Analysis | |
| 505 | 8 | |a The confusion behind networks -- What is this network stuff? -- Graph theory -- Social network analysis -- Network science -- Resources for learning about network analysis -- Notebook interfaces -- IDEs -- Network datasets -- Kaggle datasets -- NetworkX and scikit-network graph generators -- Creating your own datasets -- NetworkX and articles -- Common network use cases -- Mapping production dataflow -- Mapping community interactions -- Mapping literary social networks -- Mapping historical social networks -- Mapping language -- Mapping dark networks -- Market research -- Finding specific content | |
| 505 | 8 | |a Creating ML training data -- Advanced network use cases -- Graph ML -- Recommendation systems -- Getting started with networks -- Example -- K-pop implementation -- Summary -- Further reading -- Chapter 3: Useful Python Libraries -- Technical requirements -- Using notebooks -- Data analysis and processing -- pandas -- NumPy -- Data visualization -- Matplotlib -- Seaborn -- Plotly -- NLP -- Natural Language Toolkit -- Setup -- Starter functionality -- Documentation -- spaCy -- Network analysis and visualization -- NetworkX -- scikit-network -- ML -- scikit-learn -- Karate Club -- spaCy (revisited) | |
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