Graph Machine Learning
Take graph data to the next level by applying machine learning techniques and algorithms
CSAMCreated by Enrico Deusebio, Claudio Stamile, Aldo Marzullo
Explore how to unlock the full potential of network data by applying machine learning techniques to graphs. Learn practical methods for processing, modeling, and analyzing relationships between entities to solve real-world problems. Gain hands-on experience building and scaling graph-based machine learning applications.
Packt | Jun 2021 | 338 min
What You Will Learn
You will start by learning the fundamentals of graph theory and how graphs represent topological information. Through practical examples and step-by-step explanations, you will apply graph machine learning models to real datasets. As you progress, you will build complete pipelines and explore advanced techniques for extracting insights from complex networks.
Key Features
- Build machine learning models that leverage relationships in graph data
- Apply graph-based techniques to real-world scenarios like social networks and finance
- Understand and implement both supervised and unsupervised graph learning methods
Target Audience
Designed for data scientists, analysts, and professionals familiar with graph data and machine learning basics. If you have intermediate Python skills and want to boost your ability to analyze and model relationships within network data, this course will help you apply advanced techniques to real-world challenges and build scalable, data-driven applications.
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