Graph-Powered Machine Learning, Video Edition

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Posted on 17 Apr 13:05 | by AD-TEAM | 39 views

Graph-Powered Machine Learning, Video Edition

English | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch | Duration: 12H 34M | Size: 4.25 GB
Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data

In Graph-Powered Machine Learning you will learn
The lifecycle of a 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
Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You'll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices. Author Alessandro Negro's extensive experience shines through in every chapter, as you learn from examples and concrete scenarios based on his work with real clients!

about the technology
Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. Graph-based machine learning techniques offer a powerful new perspective for machine learning in social networking, fraud detection, natural language processing, and recommendation systems.

about the book
Graph-Powered Machine Learning teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine learning algorithms and tools. In this authoritative book, you'll master the architectures and design practices of graphs, and avoid common pitfalls. Author Alessandro Negro explores examples from real-world applications that connect GraphML concepts to real world tasks.

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