Graph-Powered Machine Learning
This book from Manning Publications is a wonderful introduction to graphs for machine learning enthusiasts, as well as a great entrée into machine learning for graph experts.
Machine learning is an incredibly powerful tool for any task involving pattern matching. Some applications, however, such as identifying fraud, detecting network intrusions, mining social network data, and smart search are more efficient when you treat the underlying data as a graph!
Graph-Powered Machine Learning, written by GraphAware Chief Scientist and experienced speaker Alessandro Negro and published by Manning Publications, teaches you how to use graph-based algorithms and data organization strategies to develop powerful machine learning applications for your projects.
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. Alessandro Negro’s extensive experience building with graph-based machine learning systems shines through in every chapter as you learn from examples and concrete scenarios based on his own work with real clients.
Interested? This book is available now with Manning’s Early Access Program (MEAP) letting you read the chapters as they’re written. Use the discount code kdngpml50 at checkout to save 50% off your purchase! Discount code valid through December 31st, 2018.