Edité par Packt Publishing (edition ), 2021
ISBN 10 : 1800204493 ISBN 13 : 9781800204492
Langue: anglais
Vendeur : BooksRun, Philadelphia, PA, Etats-Unis
EUR 26,44
Autre deviseQuantité disponible : 1 disponible(s)
Ajouter au panierPaperback. Etat : Very Good. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
Vendeur : Swan Trading Company, GEORGETOWN, TX, Etats-Unis
EUR 29,16
Autre deviseQuantité disponible : 1 disponible(s)
Ajouter au panierpaperback. Etat : Very Good. Softcover shows only light cover wear. Text is unmarked and binding tight. Ships FAST!
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
EUR 52,38
Autre deviseQuantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : New. In.
Vendeur : California Books, Miami, FL, Etats-Unis
EUR 51,39
Autre deviseQuantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : New.
Vendeur : California Books, Miami, FL, Etats-Unis
EUR 53,13
Autre deviseQuantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : New.
Edité par Packt Publishing 2021-06-25, 2021
ISBN 10 : 1800204493 ISBN 13 : 9781800204492
Langue: anglais
Vendeur : Chiron Media, Wallingford, Royaume-Uni
EUR 50,61
Autre deviseQuantité disponible : 10 disponible(s)
Ajouter au panierPaperback. Etat : New.
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
EUR 45,92
Autre deviseQuantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : New.
Edité par Packt Publishing Limited, GB, 2021
ISBN 10 : 1800204493 ISBN 13 : 9781800204492
Langue: anglais
Vendeur : Rarewaves.com UK, London, Royaume-Uni
EUR 64,41
Autre deviseQuantité disponible : Plus de 20 disponibles
Ajouter au panierPaperback. Etat : New. 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.
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
EUR 49,56
Autre deviseQuantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : As New. Unread book in perfect condition.
Vendeur : Best Price, Torrance, CA, Etats-Unis
EUR 41,10
Autre deviseQuantité disponible : 2 disponible(s)
Ajouter au panierEtat : New. SUPER FAST SHIPPING.
Vendeur : Best Price, Torrance, CA, Etats-Unis
EUR 42,63
Autre deviseQuantité disponible : 2 disponible(s)
Ajouter au panierEtat : New. SUPER FAST SHIPPING.
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
EUR 52,37
Autre deviseQuantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : New.
Edité par Packt Publishing Limited, GB, 2021
ISBN 10 : 1800204493 ISBN 13 : 9781800204492
Langue: anglais
Vendeur : Rarewaves.com USA, London, LONDO, Royaume-Uni
EUR 69,57
Autre deviseQuantité disponible : Plus de 20 disponibles
Ajouter au panierPaperback. Etat : New. 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.
EUR 60,80
Autre deviseQuantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : New. 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 th.
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
EUR 56,39
Autre deviseQuantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : As New. Unread book in perfect condition.
Vendeur : Dream Books Co., Denver, CO, Etats-Unis
EUR 27,20
Autre deviseQuantité disponible : 1 disponible(s)
Ajouter au panierEtat : acceptable. This copy has clearly been enjoyedâ"expect noticeable shelf wear and some minor creases to the cover. Binding is strong, and all pages are legible. May contain previous library markings or stamps.
Vendeur : Books Puddle, New York, NY, Etats-Unis
EUR 84,91
Autre deviseQuantité disponible : 4 disponible(s)
Ajouter au panierEtat : New.
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
EUR 44,75
Autre deviseQuantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : New.
Vendeur : Mispah books, Redhill, SURRE, Royaume-Uni
EUR 85,47
Autre deviseQuantité disponible : 1 disponible(s)
Ajouter au panierpaperback. Etat : New. New. book.
Vendeur : Majestic Books, Hounslow, Royaume-Uni
EUR 87,01
Autre deviseQuantité disponible : 4 disponible(s)
Ajouter au panierEtat : New. Print on Demand.
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
EUR 90,87
Autre deviseQuantité disponible : 4 disponible(s)
Ajouter au panierEtat : New. PRINT ON DEMAND.