Graph Mining in Arabic - تنقيب الرسم البياني
Unlocking the Power of Arabic Language Data: Learn Graph Mining Techniques
What you will learn
Students will learn the fundamentals of graph theory and its various applications in data science and machine learning.
Students will gain experience in using popular graph mining tools and libraries such as NetworkX and Neo4j.
Students will learn how to use graph algorithms such as centrality measures, community detection, and link prediction to analyze and interpret real-world networ
Students will be able to implement graph-based models and apply them to solve real-world problems such as recommendation systems, social network analysis.
Why take this course?
This course on Graph Mining in Arabic is designed to help professionals, students, and researchers gain a deeper understanding of graph mining techniques and how to apply them to Arabic language data. The course covers the basics of graph theory and provides an overview of natural language processing and machine learning concepts. You will learn how to represent, analyze and visualize Arabic texts using graph mining techniques.
Throughout the course, you will work with real-world examples and datasets to gain hands-on experience with the techniques covered. You will learn how to use popular Python libraries such as Networkx and Matplotlib to analyze and visualize Arabic text data. You will also learn how to use graph mining techniques to extract insights from Arabic text data and gain a deeper understanding of the underlying structure of the data.
By the end of the course, you will have a solid understanding of graph mining techniques and be able to apply them to Arabic language data. You will be able to use this knowledge to unlock the power of Arabic language data in your own work and research.
This course is suitable for professionals, students, and researchers who are interested in working with Arabic language data and have a basic understanding of graph theory, computer science, data science, or related fields.