Graph-Powered Machine Learning

by Dr. Alessandro Negro

Graph-Powered Machine Learning introduces you to graph technology concepts, highlighting the role of graphs in machine learning and big data platforms. You’ll get an in-depth look at techniques including data source modeling, algorithm design, link analysis, classification, and clustering. As you master the core concepts, you’ll explore three end-to-end projects that illustrate architectures, best design practices, optimization approaches, and common pitfalls.

What's inside

  • The lifecycle of a machine learning project
  • Three end-to-end applications
  • Graphs in big data platforms
  • Data source modeling
  • Natural language processing
  • Recommendations and relevant search
  • Optimization methods

About the author

Dr. Alessandro Negro is Chief Scientist at GraphAware & MD at GraphAware Italy. Holding a Ph.D. in Interdisciplinary Science and Technology, he is a Data Scientist & Software Architect. Alessandro can be found speaking at various conferences through the year & after publishing numerous articles in blogs & magazines, is now also an accomplished author of the aforementioned book Graph-Powered Machine Learning. He is also instrumental in building Hume – the cutting edge insights engine by GraphAware.

GraphAware Hume: Graph-Powered Machine Learning In Action

Hume is an NLP-focused, Graph-Powered Insight Engine, where you can see the book come to life & used in real-world problem solving. Applications include security concerns like identifying fraud or detecting network intrusions, application areas like social networking or natural language processing, and better user experiences through accurate recommendations and smart search. If this is of interest to you, set up a one-on-one with our experts & learn how Hume can help you gain a competitive edge.

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