Graph algorithms for the Data Science course. Graphs are a natural way to display and understand related data. This course covers the most important graph-related algorithms and techniques in the field of data science, and provides practical advice on their implementation and application. Even if you have no experience with charts, you can benefit from this valuable guide. These powerful algorithms are explained using simple text and jargon-free images, making them easy to apply to your own projects.
What you will learn:
- Graph Modeling with Labeled Features
- Create graphs from structured data such as CSV or SQL.
- Natural Language Processing (NLP) Techniques for Generating Graphs from Unstructured Data
- Cryptographic query language syntax for data manipulation and information retrieval.
- Social network analysis algorithms such as PageRank and community detection.
- How to transform graph structure into machine learning model input using node embedding models
- Using graph functions in problems of node classification and link prediction.
This course provides practical guidance on working with graphical data in applications such as machine learning, fraud detection, and business data analytics. The course is full of interesting and engaging projects that will teach you the graphing alphabet. You’ll gain practical skills by analyzing Twitter, creating graphs using NLP techniques, and much more.
This course is suitable for people who:
- Have basic knowledge of machine learning.
- Become familiar with the Cypher query language covered in this course (optional).