Graph Machine Learning
Data scientists working with network data will be able to put their knowledge to work with this practical guide to building machine learning algorithms using graph data. The book provides a hands-on approach to implementation and associated methodologies that will have you up and running and productive in no time.
Offered by
Difficulty Level
Intermediate
Completion Time
11h16m
Language
English
About Book
Who Is This Book For?
This book is for data scientists, data analysts, graph analysts, and graph professionals who want to leverage the information embedded in the connections and relations between data points to boost their analysis and model performance using machine learning. It will also be useful for machine learning developers or anyone who wants to build ML-driven graph databases. A beginner-level understanding of graph databases and graph data is required, alongside a solid understanding of ML basics. You’ll also need intermediate-level Python programming knowledge to get started with this book.
Graph Machine Learning
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 11h16m total length
Getting Started with Graphs
Graph Machine Learning
Unsupervised Graph Learning
Supervised Graph Learning
Problems with Machine Learning on Graphs
Social Network Graphs
Text Analytics and Natural Language Processing Using Graphs
Graph Analysis for Credit Card Transactions
Building a Data-Driven Graph-Powered Application
Novel Trends on Graphs
Related Resources
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