Graph Machine Learning
Cód:
491_9781800204492
De: R$ 0,00Por: R$ 398,81ou X de
Build machine learning algorithms using graph data and efficiently exploit topological information within your modelsKey Features:Implement 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 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.Youll 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. Youll 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, youll 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. Youll 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 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 sup
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