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.
- 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
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.