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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

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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.

Who is this book 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.

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

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Frequently bought together

Causal Inference and Discovery in Python

$53.99

Interpretable Machine Learning with Python

$49.99

$49.99

Total $ 153.97

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Amazon verified reviews

Ben Bearden Mar 21, 2023

I've had an awesome time reading and applying the concepts of David Knickerbocker's book "Network Science with Python" to my own research.Above is a social network of Russian propaganda channels on Telegram that I've made using the techniques he laid out in his book.It is still a work in progress, but I am baffled by the applicability of network science to OSINT and research in general.Still reading but am thoroughly enjoying it thus far!

Amazon Verified review

Deep P. Mar 11, 2023

"Network Science with Python" by David Knickerbocker is a fascinating and comprehensive guide to network science. This book explores the principles and applications of network science, including social network analysis, and machine learning.The author uses clear and concise language to explain complex concepts in network science, making the book accessible to a wide range of readers. It goes through the beginner-level concepts of Natural Language Processing (NLP) and also explains some important Python libraries to perform and develop the NLP and Machine Learning algorithms.The other important sections of the book take us into the field of Graph Construction and its cleanup. It also explores the Social Networking Analysis and some of its detailed sections such as (Egocentric Network Analysis, and Community Detection).Overall, "Network Science with Python" is an excellent comprehensive guide to the field, suitable for both students and researchers. The book is well-written, engaging, and thought-provoking, making it a must-read for anyone interested in the science of networks with python.

Amazon Customer Apr 06, 2023

This is a great book for learning network science using networkx library package in python. The author use NLP to extract the entities from social sources such as twitter and build the network based on the entities relation. Eventually using ML techniques to come up with community detection using karate club package. Although the focus of the author is on social network but all the techniques regarding the network analysis such as centralities and degrees could be use in other sort of networks. I highly recommend this book for the readers interested in learning network science.

Sangeetha May 10, 2023

🚀 Network Science with Python by David Knickerbocker 🚀First, the preface in the book was a very humble thank-you note, Love it! Made me think to learn and write more!🪄 Influence of network on us every day. I love how RLHF (Reinforcement Learning with Human Feedback) was put as “Human guided Science” in one of the articles I came across, similarly, the impact of networking in Language (Network Science in NLP) is something I was very interested in. Network analysis has a great scope in Social Media Data Mining - which in turn might need some granular insights while uncovering large search volume indices.✍🏻 Making the less obvious friends meet - NLP and networks: Uncovering the underlying relationship in texts. While it may not be very evident in the generative AI phase, to perform analysis in every sentence generated from the language models, there is a good scope of uncovering the relationship that might enhance the “missing knowledge”. Very nicely put - If there are sequences in data and it's down to words or not - get an NLP technique to process it!Figuring out relationships: Though the language domain space is possibly seeming “infinite”, there are still some “templates” surrounding it. Similar to how David mentioned questions of “Who, what, When, Where, Why, How” can be used to draw relationships.✍🏻 The scope of “insights” to “actionable insights is the sweet spot of the conversational AI domain to understand the customers and take actions based on what they want and not based on what's easy to deliver. I loved the history of NLP rooted in “sequence analysis” and his use cases with NLP solutions for the problems experienced. While there are many NLP tasks available, it is important to select the task that best addresses the problem at hand and avoid becoming overwhelmed by attempting to apply every available technique.I am planning to read one chapter a day to consume the impact of Network Science in the NLP domain. It was an insightful start of reminding the important aspects of NLP and text analysis while I was moving into the generative AI landscape of "controlling the text generation that I can't really control" - prompts. I have been reading about memory-assisted agents too in conversational AI, thinking memory as a distributed network that could be pieced out and ensembled together is something I want to experiment!🚀 Thank you Packt Vinishka Kalra for sending the book! Appreciate it!I would love to study the exploration of conversations, and relationships and how they can serve as a better context memory for referencing conversations, how the links and relationships would help to decipher an initial look towards the "Missing text phenomenon" that language models in some cases struggle to decode.#networkscience #nlp

S M Mar 13, 2023

David Knickerbocker does an excellent job of explaining complex concepts in an accessible and easy-to-understand manner. The book is well-organized, and the examples are clear and concise, making it easy to follow along, even if you're not an expert in network science or Python.One of the things I appreciated most about this book is that it teaches you how to analyze and understand networks and provides real-world applications for this knowledge. The book covers a range of topics, including social network analysis, machine learning, and graph theory.The code examples provided in the book are easy to follow and provide a solid foundation for anyone looking to dive deeper. I loved playing around with the Twitter Python library, for instance.Overall, I highly recommend "Network Science with Python" to anyone interested in network analysis, or it's predictive applications with machine learning. The author's clear writing style, well-organized structure, and practical examples make this book an essential resource for anyone looking to understand and analyze the networks around us.

Network Science with Python: Explore the networks around us using network science, social network analysis, and machine learning

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