Over 10,000 physical typewritten documents from 1932 to 1941 had to be digitised, structured, and connected in order to create a single, centralised source of knowledge, for enabling the analysis of historical processes.
At its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as graphs. Graph-Powered Machine Learning teaches you how to use graph-based algorithms and data organization strategies to develop superior machine learning applications.
Read the interview with our Chief Data Scientist Alessandro Negro published on Neo4j blog, where he talks about how GraphAware uses natural language processing to help companies gain a better understanding of the knowledge that is spread across their organization.
