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  <img class="date-icon" src="/icons/outline/date-blue.svg" alt="Calendar" />  Module 5 
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# Course Summary

This module completes the Qdrant Beginners course. Here's what was covered:

| Module | Theme | Key concepts covered |
|--------|-------|----------------------|
| Module 1 | Let's Understand Search | Why keyword search fails; how embeddings and semantic search work; the shift from words to meaning. |
| Module 2 | First Principles of Vector Search | Collections, points, vectors, payloads, HNSW, chunking strategies, and the full ingestion pipeline. |
| Module 3 | Sparse vs Dense vs Hybrid Search | BM25 against embeddings; when each fails; hybrid search with rank fusion. |
| Module 4 | Designing a Vector Search System | The five layers of the stack; what to decide before ingesting; what changes as the collection grows; when to add machines; where generation fits; where to run Qdrant. |
| Module 5 | Multimodal Supplier Risk Intelligence | End-to-end capstone: ingest news, transcripts, and images on shared points; cluster risk signals; query every modality. |
| Module 6 | Beyond Similarity (Bonus) | Optional further reading: score boosting, MMR diversity, two-stage reranking, grouping, relevance feedback, and discovery. |

Next, [get #QdrantCertified](https://qdrant.tech/course/beginners/certification/index.md) with the official Beginners exam, which covers Modules 1 through 5.