Curated learning path for Knowledge Graphs & Graph AI. Build practical skills through expert-selected courses.
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Basic statistics helpful; will be taught
Some coding experience; Python or R preferred
Knowledge Graphs: Foundations and Applications
IntermediateText Classification and Named Entity Recognition (NER) with NLP Training Course
IntermediateTraining Course on Named Entity Recognition (NER) and Information Extraction
AdvancedAI-Enhanced Photography: From Capture to Creation
IntermediateAI-Powered Graphic Design
IntermediateKnowledge Graphs: Foundations and Applications
IntermediateText Classification and Named Entity Recognition (NER) with NLP Training Course
IntermediateTraining Course on Named Entity Recognition (NER) and Information Extraction
AdvancedAI-Enhanced Photography: From Capture to Creation
IntermediateAI-Powered Graphic Design
IntermediateFollow these courses in order to complete the learning path. Click on any course to enroll.
This course provides a comprehensive overview of knowledge graphs, their underlying technologies, and their significance in today's digital world. You will learn what is necessary to design, implement, and apply knowledge graphs, with a focus on basic semantic technologies including RDF, OWL, and SPARQL.
This training covers both text classification and NER, explaining entity types and the differences between rule-based and machine learning approaches. It includes hands-on labs for implementing NER with spa Cy and customizing models for specific applications.
This course provides a deep dive into NER and Information Extraction, equipping participants with NLP techniques to transform unstructured text into structured data. It focuses on advanced machine learning and deep learning architectures, including Transformer models.
AI-Enhanced Photography: From Capture to Creation
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Curated learning path for Knowledge Graphs & Graph AI. Build practical skills through expert-selected courses.
You'll work through Understand core concepts and foundations; Build your first projects with guided tutorials; Gain confidence with hands-on exercises; Perform exploratory data analysis (EDA) on complex datasets; Clean and preprocess data for analysis; Create compelling data visualizations.
About 38 hours of study across 5 courses — and you can go at your own pace.
It's pitched at beginner level — a solid starting point if you're new to the topic.
5 curated courses, sequenced from foundational to advanced.
The courses in this path can be started for free.
Enroll in this path to track your progress and stay motivated.