Items related to Graph Machine Learning: Take graph data to the next...
Graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms - Softcover
Synopsis
Build machine learning algorithms using graph data and efficiently exploit topological information within your models
Key Features:
- Implement machine learning techniques and algorithms in graph data
- Identify the relationship between nodes in order to make better business decisions
- Apply graph-based machine learning methods to solve real-life problems
Book Description:
Graph Machine Learning will introduce you to a set of tools used for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks.
The first chapters will introduce you to graph theory and graph machine learning, as well as the scope of their potential use.
You'll then learn all you need to know about the main machine learning models for graph representation learning: their purpose, how they work, and how they can be implemented in a wide range of supervised and unsupervised learning applications. You'll build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data.
After covering the basics, you'll be taken through real-world scenarios such as extracting data from social networks, text analytics, and natural language processing (NLP) using graphs and financial transaction systems on graphs. You'll also learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, and explore the latest trends on graphs.
By the end of this machine learning book, you will have learned essential concepts of graph theory and all the algorithms and techniques used to build successful machine learning applications.
What You Will Learn:
- Write Python scripts to extract features from graphs
- Distinguish between the main graph representation learning techniques
- Learn how to extract data from social networks, financial transaction systems, for text analysis, and more
- Implement the main unsupervised and supervised graph embedding techniques
- Get to grips with shallow embedding methods, graph neural networks, graph regularization methods, and more
- Deploy and scale out your application seamlessly
Who this book is 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.
"synopsis" may belong to another edition of this title.
About the Author
Claudio Stamile received an M.Sc. degree in computer science from the University of Calabria (Cosenza, Italy) in September 2013 and, in September 2017, he received his joint Ph.D. from KU Leuven (Leuven, Belgium) and Université Claude Bernard Lyon 1 (Lyon, France). During his career, he has developed a solid background in artificial intelligence, graph theory, and machine learning, with a focus on the biomedical field. He is currently a senior data scientist in CGnal, a consulting firm fully committed to helping its top-tier clients implement data-driven strategies and build AI-powered solutions to promote efficiency and support new business models.
Aldo Marzullo received an M.Sc. degree in computer science from the University of Calabria (Cosenza, Italy) in September 2016. During his studies, he developed a solid background in several areas, including algorithm design, graph theory, and machine learning. In January 2020, he received his joint Ph.D. from the University of Calabria and Université Claude Bernard Lyon 1 (Lyon, France), with a thesis entitled Deep Learning and Graph Theory for Brain Connectivity Analysis in Multiple Sclerosis. He is currently a postdoctoral researcher at the University of Calabria and collaborates with several international institutions.
Enrico Deusebio is currently the chief operating officer at CGnal, a consulting firm that helps its top-tier clients implement data-driven strategies and build AI-powered solutions. He has been working with data and large-scale simulations using high-performance facilities and large-scale computing centers for over 10 years, both in an academic and industrial context. He has collaborated and worked with top-tier universities, such as the University of Cambridge, the University of Turin, and the Royal Institute of Technology (KTH) in Stockholm, where he obtained a Ph.D. in 2014. He also holds B.Sc. and M.Sc. degrees in aerospace engineering from Politecnico di Torino.
"About this title" may belong to another edition of this title.
Search results for Graph Machine Learning: Take graph data to the next...
Stock Image
Graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms
Seller: medimops, Berlin, Germany
Seller rating 5 out of 5 stars
Condition: very good. Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages. Seller Inventory # M01800204493-V
Quantity: 1 available
Stock Image
Graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms
Published by
Packt Publishing (edition ), 2021
ISBN 10: 1800204493
ISBN 13: 9781800204492
Used
Paperback
Seller: BooksRun, Philadelphia, PA, U.S.A.
Seller rating 5 out of 5 stars
Paperback. Condition: Very Good. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported. Seller Inventory # 1800204493-8-1
Quantity: 1 available
Stock Image
Graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms
Seller: Swan Trading Company, GEORGETOWN, TX, U.S.A.
Seller rating 5 out of 5 stars
paperback. Condition: Very Good. Softcover shows only light cover wear. Text is unmarked and binding tight. Ships FAST! Seller Inventory # 2411180026
Quantity: 1 available
Stock Image
Graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition. Seller Inventory # 43019575
Quantity: Over 20 available
Stock Image
Graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # 43019575-n
Quantity: Over 20 available
Stock Image
Graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # ABLIING23Mar2912160210163
Quantity: Over 20 available
Seller Image
Graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms (Paperback or Softback)
Published by
Packt Publishing 6/25/2021, 2021
ISBN 10: 1800204493
ISBN 13: 9781800204492
New
Paperback or Softback
Seller: BargainBookStores, Grand Rapids, MI, U.S.A.
Seller rating 5 out of 5 stars
Paperback or Softback. Condition: New. Graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms 1.28. Book. Seller Inventory # BBS-9781800204492
Quantity: 5 available
Stock Image
Graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms
Seller: California Books, Miami, FL, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # I-9781800204492
Quantity: Over 20 available
Stock Image
Graph Machine Learning
Print on DemandSeller: PBShop.store US, Wood Dale, IL, U.S.A.
Seller rating 5 out of 5 stars
PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Seller Inventory # L0-9781800204492
Quantity: Over 20 available
Stock Image
Graph Machine Learning (Paperback)
Published by
Packt Publishing Limited, Birmingham, 2021
ISBN 10: 1800204493
ISBN 13: 9781800204492
New
Paperback
Seller: Grand Eagle Retail, Mason, OH, U.S.A.
Seller rating 5 out of 5 stars
Paperback. Condition: new. Paperback. Build machine learning algorithms using graph data and efficiently exploit topological information within your modelsKey FeaturesImplement machine learning techniques and algorithms in graph dataIdentify the relationship between nodes in order to make better business decisionsApply graph-based machine learning methods to solve real-life problemsBook DescriptionGraph Machine Learning will introduce you to a set of tools used for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks.The first chapters will introduce you to graph theory and graph machine learning, as well as the scope of their potential use.You'll then learn all you need to know about the main machine learning models for graph representation learning: their purpose, how they work, and how they can be implemented in a wide range of supervised and unsupervised learning applications. You'll build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data.After covering the basics, you'll be taken through real-world scenarios such as extracting data from social networks, text analytics, and natural language processing (NLP) using graphs and financial transaction systems on graphs. You'll also learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, and explore the latest trends on graphs.By the end of this machine learning book, you will have learned essential concepts of graph theory and all the algorithms and techniques used to build successful machine learning applications.What you will learnWrite Python scripts to extract features from graphsDistinguish between the main graph representation learning techniquesLearn how to extract data from social networks, financial transaction systems, for text analysis, and moreImplement the main unsupervised and supervised graph embedding techniquesGet to grips with shallow embedding methods, graph neural networks, graph regularization methods, and moreDeploy and scale out your application seamlesslyWho this book is forThis 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781800204492
Quantity: 1 available