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| # Tutorial Syllabus | |
| <img src="../assets/nouns/tutorial.png" alt="Video Tutorial by artworkbean from the Noun Project" /> | |
| Coding samples in the following notebooks help illustrate the use | |
| of **TextGraphs** and related libraries in Python. | |
| ## Audience | |
| * You are a Python programmer who needs to learn how to leverage LLM-augmented workflows to construct KGs | |
| * You are an ML engineer who needs to understand how to integrate LLM research results into production-quality apps | |
| ## Prerequisites | |
| * Some coding experience in Python (you can read a 20-line program) | |
| * Some familiarity with ML, specifically with LLM applications | |
| * Interest in use cases that need to use NLP to construct KGs | |
| ## Key Takeaways | |
| * Hands-on experience with popular open source libraries in Python for natural language at the intersection of LLMs and KGs | |
| * Coding examples that can be used as starting points for your own related projects | |
| * Ways to integrate natural language work with other aspects of graph data science | |