Warning
These are brief reviews for personal reference, summarizing the pros and cons of key points and methodologies covered in the course. These are subjective opinions, for a more detailed and better insight into the course, please consider enrolling in the course.
Review: Preprocessing Unstructured Data for LLM Applications¶
Overall Rating: ⭐️⭐️⭐️☆☆¶
Overall, it's a great course for understanding various approaches to different sources of external information in unstructured data.
Topics Covered: ⭐️⭐️⭐️⭐️☆¶
This course does a great job of covering the need to read unstructured data, different formats that are used, how to think about extracting information from different structured and unstructured data formats, what methods are used for extraction (traditional vs ML-based), and how to use the extracted data in Retrieval Augmented Generation (RAG).
Methodology Covered: ⭐️⭐️⭐️☆☆¶
The downside is that almost all extraction is done via API calls to unstructured data, so there is hardly any code-based explanation of what is happening. There are only occasional hints here and there about the ML models being used (e.g., YOLOX for document analysis).
Recommended Audience¶
- Those new to working with unstructured data and unaware of how to structure and serialize it
- Professionals looking to understand different approaches to extracting data from structured vs unstructured data formats
- Individuals aiming to apply hybrid search methods (semantic search + metadata based filtering) in RAG applications
- Those looking for in-depth code-based explanations on the exact extraction approaches
- Researchers/Developers seeking detailed explanations of ML model based approaches for data extraction
- Individuals already proficient in Unstructured API-based data extraction