Discover graph-powered machine learning techniques, including data source modelling, algorithm design, link analysis, classification, and clustering.
While mastering core concepts, you’ll delve into three end-to-end projects illustrating architectures, best practice designs, optimisation approaches, and common pitfalls.
You will learn:
- The lifecycle of a graph-powered machine learning project
- The role of graphs in big data platforms
- Data source modelling using graphs
- Graph-based natural language processing, recommendations, and fraud detection techniques
- Graph algorithms
- Working with Neo4J
Meet the author
Alessandro Negro has extensive experience in the domain of building and analysing knowledge graphs. He covers expertise in engineering, research, data science and consultancy, all delivered for and with clients operating in a wide span of industries all around the world.
Currently he is contributing to building an enterprise-level product for mission-critical graph analytics – GraphAware Hume.
Alessandro Negro
Chief Scientist
“I learned so much from this unique and comprehensive book. A real gem for anyone who wants to explore graph-powered ML apps.”
Helen Mary Labao-Barrameda, Okada Manila
