What you would learn in Graph-Powered Machine Learning, Video Edition course?
Graphic-Powered Machine-Learning instructs users to utilize graph-based algorithms and data management strategies to create top-quality machine learning software. We will explore the role of graphs in machine-learning and big data platforms. You'll also explore in-depth algorithms, data source modeling, design, recommendations, and fraud detection. Explore complete projects that demonstrate structures and aid in optimizing using the best design practices. The vast experience of author Alessandro Nero shows in each chapter as you learn through examples and real-world situations based on his experiences on real projects with actual clients!
Technology and the future
Recognizing relationships is the primary goal in machine learning. Through analyzing and recognizing the connections within your data, graph-based algorithms such as PageRank and K-nearest-neighbor drastically increase the effectiveness for ML applications. Machine learning techniques based on graphs offer an exciting new approach to machine learning within social networks, fraud detection and natural language processing, and even recommendation systems.
About the book
Graph-powered Machine Learning shows you how to leverage the natural connections in unstructured and structured datasets using graph-oriented machine learning techniques. In this encyclopedia, you will learn graphs' fundamentals and design principles and avoid the most common mistakes. Alessandro Negro explores examples from real-world applications that link GraphML concepts to real-world work.
What's the inside
- Graphs on big data platforms
- Recommendations, natural language processing, fraud detection
- Graph algorithms
- Working using Neo4J graph database Neo4J graph database
Course Content:
- The life cycle of a machine learning program
- Graphs on big data platforms
- Modeling of data sources by graphs
- Natural language processing using graphs as well as recommendations and fraud detection methods
- Graph algorithms
- Working using Neo4J
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