你将学到什么
Explore large-scale networks with different structures and properties;
Learn graph representations using advanced deep learning and embedding techniques;
Utilize NLP fundamentals to build knowledge graphs;
Use knowledge graphs in modern search applications;
Model knowledge graphs using embedding methods.
课程概况
Many real-world datasets come in the form of graphs. These datasets include social networks, biological networks, knowledge graphs, the World Wide Web, and many more. Having a comprehensive understanding of these networks is essential to truly understand many important applications.
This course introduces the fundamental concepts and tools used in modeling large-scale graphs and knowledge graphs. You will learn a spectrum of techniques used to build applications that use graphs and knowledge graphs. These techniques range from traditional data analysis and mining methods to the emerging deep learning and embedding approaches.
课程大纲
Module 1: Introduction and Overview
Module 2: Graph Properties and Applications
Module 3: Graph Representation Learning
Module 4: Knowledge Graph Fundamentals and Construction
Module 5: Knowledge Graph Inference and Applications
预备知识
Advanced math skills
Basic programming skills
Fundamental knowledge on machine learning and deep learning techniques
Skills equivalent to the following course on big data analysis
DAT223.1x:Processing Big Data with Azure Data Lake Analytics
常见问题
Do I need an Azure subscription to complete the course?
Yes. An Azure subscription is required to complete the hands-on labs in this course.
Will Microsoft provide a free Azure subscription for students in this course?
No, but you can sign up for a free 30-day trial of Azure, or engage in various Microsoft programs that include limited free access to Azure. You can sign up for a free Azure subscription only once, and a credit card may be required to authenticate your identity. Other conditions may also apply.
Do I need a Windows computer to complete the course?
No. You can complete the labs using a computer running Windows, Mac OS X, or Linux
