Discover graph powered machine learning techniques including data source modeling, algorithm design, link analysis, classification, and clustering. While mastering core concepts, you’ll delve into three end-to-end projects illustrating architectures, best design practices, optimization approaches, and common pitfalls.
In Graph-Powered Machine Learning, you will learn:
- The lifecycle of a graph powered machine learning project
- Graphs in big data platforms
- Data source modeling using graphs
- Graph-based natural language processing, recommendations, and fraud detection techniques
- Graph algorithms
- Working with Neo4J
Meet the author
Alessandro Negro has an extensive experience in the domain of building and analyzing 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
