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Andrei Pruteanu
This course will teach you how to create knowledge graphs out of textual information. It will show you how to extract information such as topics and entities and uncover how they are linked into so-called knowledge graphs.
Read more
This course will teach you how to create knowledge graphs out of textual information. It will show you how to extract information such as topics and entities and uncover how they are linked into so-called knowledge graphs.
Read more
This course will teach you how to create knowledge graphs out of textual information. It will show you how to extract information such as topics and entities and uncover how they are linked into so-called knowledge graphs.
In this course, Building Knowledge Graphs Using Python, you’ll learn how to extract and link information by creating graphs out of textual data. First, you will explore how to do topic modeling using Python. Next, you will discover how to do entity extraction. Finally, you will learn how to link the information uncovered in the previous two steps in a unified manner. When you’re finished with this course, you’ll have the skills and knowledge of creating knowledge graphs using Python needed to uncover complex information out of raw textual data. All code is available on GitHub: https://github.com/andreipruteanu/pluralsight
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What's inside
Syllabus
Course Overview
Getting Started
Preprocessing Data
Topic Modeling Using Python
Read more
Syllabus
Course Overview
Getting Started
Preprocessing Data
Topic Modeling Using Python
Read more
Entity Extraction Using Python
Creating Knowledge Graphs Using Python
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops topic modeling, entity extraction, and knowledge graph creation, which are core skills for data science
Taught by Andrei Pruteanu, who is recognized for their work in knowledge graphs and natural language processing
Teaches extraction and linking of information out of textual data, which is highly relevant to machine learning, data mining, and business intelligence
Offers hands-on labs and interactive materials, which reinforce learning and improve retention
Provides a strong foundation for beginners in knowledge graph creation and natural language processing
Requires learners to come in with some background knowledge in Python and natural language processing, which may be a barrier for some learners
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Building Knowledge Graphs with Python with these
activities:
Review fundamental data structures and algorithms
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Reviewing these fundamental computer science concepts will help strengthen your foundational understanding of knowledge graphs.
Browse courses on
Data Structures
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Revisit textbooks and lecture notes from previous courses on data structures and algorithms.
-
Practice solving coding problems on platforms like LeetCode or HackerRank.
Show all one activities
Review fundamental data structures and algorithms
Show steps
Reviewing these fundamental computer science concepts will help strengthen your foundational understanding of knowledge graphs.
Browse courses on
Data Structures
Show steps
Show steps
Show steps
- Revisit textbooks and lecture notes from previous courses on data structures and algorithms.
- Practice solving coding problems on platforms like LeetCode or HackerRank.
Show all one activities
Career center
Learners who complete Building Knowledge Graphs with Python will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use scientific methods to extract knowledge and insights from data. Taking the course *Building Knowledge Graphs with Python* can be beneficial for Data Scientists as it teaches how to create knowledge graphs from textual data. Knowledge graphs can be used to represent complex relationships between data points, which can be valuable for tasks such as data mining and machine learning.
Data Analyst
Data Analyst
Data Analysts gather, clean, and interpret data to draw meaningful conclusions. By taking the course *Building Knowledge Graphs with Python*, you will learn how to extract information from textual data and represent it in a structured way, which is a valuable skill for Data Analysts. This course focuses on creating knowledge graphs, a type of structured data that can be used for various applications, including data analysis.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. The course *Building Knowledge Graphs with Python* provides foundational knowledge in creating knowledge graphs, which can be used as input for machine learning models. By understanding how to extract and structure information from text, Machine Learning Engineers can improve the accuracy and efficiency of their models.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. Taking the course *Building Knowledge Graphs with Python* can be beneficial for Software Engineers who work on natural language processing or data-intensive applications. The course provides hands-on experience in extracting and structuring information from text, which can be valuable for building software solutions that can understand and process natural language data.
Knowledge Engineer
Knowledge Engineer
Knowledge Engineers design and build knowledge-based systems. The course *Building Knowledge Graphs with Python* can be beneficial for Knowledge Engineers, as it provides a practical understanding of how to represent and reason with knowledge. By learning how to create knowledge graphs, Knowledge Engineers can improve the accuracy and efficiency of knowledge-based systems.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and infrastructure. The course *Building Knowledge Graphs with Python* can provide valuable skills for Data Engineers, as it teaches how to extract, transform, and load data from various sources. By learning how to create knowledge graphs, Data Engineers can improve the quality and accessibility of data for analysis and decision-making.
Information Architect
Information Architect
Information Architects design and organize information systems to make them easy to find and use. The course *Building Knowledge Graphs with Python* can be helpful for Information Architects, as it provides a practical understanding of how to structure and represent information in a way that is both human-readable and machine-processable. By learning how to create knowledge graphs, Information Architects can improve the findability and accessibility of information for users.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers design and develop systems that can understand and process natural language. The course *Building Knowledge Graphs with Python* can provide valuable skills for Natural Language Processing Engineers, as it teaches how to extract and structure information from text. By learning how to create knowledge graphs, Natural Language Processing Engineers can improve the accuracy and efficiency of natural language processing systems.
Technical Writer
Technical Writer
Technical Writers create and maintain documentation for technical products and systems. The course *Building Knowledge Graphs with Python* may be useful for Technical Writers who work on complex technical documentation. By learning how to create knowledge graphs, Technical Writers can improve the organization and clarity of technical documentation.
Research Scientist
Research Scientist
Research Scientists conduct scientific research to advance knowledge and understanding. The course *Building Knowledge Graphs with Python* may be useful for Research Scientists who work in fields such as natural language processing or data science. By learning how to create knowledge graphs, Research Scientists can improve the efficiency and accuracy of their research.
Taxonomist
Taxonomist
Taxonomists classify and organize living organisms. The course *Building Knowledge Graphs with Python* may be useful for Taxonomists as it provides a practical understanding of how to structure and represent information in a hierarchical way. By learning how to create knowledge graphs, Taxonomists can improve the organization and accessibility of taxonomic information.
Archivist
Archivist
Archivists preserve and manage historical records. The course *Building Knowledge Graphs with Python* may be useful for Archivists who want to learn more about knowledge representation or who work with digital archives. By learning how to create knowledge graphs, Archivists can improve the organization and accessibility of historical records.
Knowledge Management Specialist
Knowledge Management Specialist
Knowledge Management Specialists develop and implement strategies for managing and sharing knowledge within organizations. The course *Building Knowledge Graphs with Python* may be useful for Knowledge Management Specialists who want to learn more about knowledge representation or who work with digital knowledge assets. By learning how to create knowledge graphs, Knowledge Management Specialists can improve the organization and accessibility of knowledge within organizations.
Museum curator
Museum curator
Museum Curators manage and interpret collections of artifacts and specimens. The course *Building Knowledge Graphs with Python* may be useful for Museum Curators who want to learn more about knowledge representation or who work with digital collections. By learning how to create knowledge graphs, Museum Curators can improve the organization and accessibility of museum collections.
Librarian
Librarian
Librarians organize and manage information resources. The course *Building Knowledge Graphs with Python* may be useful for Librarians who work with digital collections or who want to learn more about knowledge representation. By learning how to create knowledge graphs, Librarians can improve the discoverability and accessibility of information resources.
For more career information including salaries, visit:
OpenCourser.com/course/84gt9p/building
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Building Knowledge Graphs with Python.
Provides a comprehensive overview of natural language processing (NLP) techniques, including topic modeling and entity extraction. It valuable resource for anyone interested in building knowledge graphs using Python.
Provides a comprehensive overview of machine learning techniques for text data, including topic modeling and entity extraction. It valuable resource for anyone interested in building knowledge graphs using Python.
Provides a practical overview of natural language processing techniques, including topic modeling and entity extraction. It valuable resource for anyone interested in building knowledge graphs using Python.
Provides a comprehensive overview of machine learning techniques, including natural language processing. It valuable resource for anyone interested in building knowledge graphs using Python.
Provides a comprehensive overview of data analysis techniques using Python. It valuable resource for anyone interested in building knowledge graphs using Python.
Provides a comprehensive overview of the Natural Language Toolkit (NLTK), a popular library for natural language processing in Python. It valuable resource for anyone interested in building knowledge graphs using Python.
Provides a comprehensive overview of transformer-based natural language processing techniques. It valuable resource for anyone interested in building knowledge graphs using Python.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/84gt9p/building
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Time
95 hours
Level
Intermediate
Via
Pluralsight
Instructor
Andrei Pruteanu
Language
English
Good to know
Develops topic modeling, entity extraction, and knowledge graph creation, which are core skills for data science
Taught by Andrei Pruteanu, who is recognized for their work in knowledge graphs and natural language processing
Teaches extraction and linking of information out of textual data, which is highly relevant to machine learning, data mining, and business intelligence
Offers hands-on labs and interactive materials, which reinforce learning and improve retention
Provides a strong foundation for beginners in knowledge graph creation and natural language processing
Requires learners to come in with some background knowledge in Python and natural language processing, which may be a barrier for some learners
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