Название: Graph-Powered Analytics and Machine Learning with TigerGraph: Driving Business Outcomes with Connected Data (Eighth Release)Автор: Victor Lee, Phuc Kien Nguyen, Xinyu Chang
Издательство: O’Reilly Media, Inc.
Год: 2022-11-22
Страниц: 149
Язык: английский
Формат: epub
Размер: 14.96 MB
With the rapid rise of graph databases, organizations are now implementing advanced analytics and Machine Learning solutions to help drive business outcomes. This practical guide shows data scientists, data engineers, architects, and business analysts how to get started with a graph database using TigerGraph, one of the leading graph database models available.
You'll explore a three-stage approach to deriving value from connected dаta: connect, analyze, and learn. Victor Lee, Xinyu Chan, and Gaurav Deshpande from TigerGraph present real use cases covering several contemporary business needs. By diving into hands-on exercises using TigerGraph Cloud, you'll quickly become proficient at designing and managing advanced analytics and Machine Learning solutions for your organization.
This book describes property graphs. A property graph is a graph where each vertex and each edge can have properties which provide the details about individual elements. If we look again at relational databases, properties are like the columns in a table. Properties are what make graphs truly useful. They add richness and context to data which enable us to develop more nuanced queries to extract just the data that we need.
Unsupervised learning is the forgotten sibling of supervised learning and reinforcement learning, who together form the three major branches of machine learning. If you want your AI system to learn how to do a task, to classify things according to your categories, or to make predictions, you want to use supervised learning and/or reinforcement learning. Unsupervised learning, however, has the great advantage of being self-sufficient and ready-to-go. Unlike supervised learning, you don’t need to already know the right answer for some cases. Unlike reinforcement learning, you don’t have to be patient and forgiving as you stumble through learning by public trial and error. Unsupervised learning simply takes the data you have and reports what it learned.
Use graph thinking to connect, analyze, and learn from data for advanced analytics and Machine Learning
Learn how graph analytics and machine learning can deliver key business insights and outcomes
Use five core categories of graph algorithms to drive advanced analytics and machine learning
Deliver a real-time 360-degree view of core business entities, including customer, product, service, supplier, and citizen
Discover insights from connected data through machine learning and advanced analytics
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