Beginner Course
Learn the fundamentals of vector search
Understand why traditional search struggles and how modern semantic search improves it. Learn about embeddings, distance metrics, and hybrid search systems.
6 modules
From setting up dependencies to a hands-on capstone project
Shareable certificate
Earn a digital certificate upon completion
Flexible schedule
Learn at your own pace
Beginner level
No prior experience requiredWhat You’ll Learn
Skills you'll gain:
- Why traditional search struggles and how modern semantic search improves it
- How embeddings convert text to vectors that capture meaning
- Distance metrics: cosine similarity, dot product, Euclidean and Manhattan
- Hybrid search: combining dense and sparse retrieval
- Building your first Qdrant collection and queries
The Path
Module 0: Setting Up Dependencies. Configure your environment and get started with the basics.
Module 1: Let’s Understand Search. Understand why traditional search struggles and how modern semantic search improves it.
Module 2: First Principles of Vector Search. Anatomy of a vector - how data is stored, indexed, and retrieved in Qdrant.
Module 3: Sparse vs Dense vs Hybrid Search. Understand dense vs sparse search, when each fails, and how hybrid systems combine them.
Module 4: Designing a Vector Search System. How to design a vector search system - layers, filtering, RAG, and deployment.
Module 5: Capstone - Multimodal Supplier Risk Intelligence. Ingest, cluster, and query multimodal supplier signals across languages.
Bonus Module: Further Reading. A roundup of advanced techniques for further reading: score boosting, relevance feedback, MMR, and re-ranking.
How the Course Works
Bite-sized lessons
Short, friendly modules you can finish in one sitting
Learn by doing
Follow along with real examples and hands-on exercises
One step at a time
Each module builds on the last, so nothing feels out of reach
Go at your own pace
Pause anytime and pick up right where you left offSyllabus
Module 0: Setting Up Dependencies
- Qdrant Cloud Setup
- Implementing a Basic Vector Search
Module 1: Let's Understand Search
- The Problem: Why Traditional Search Struggles
- How Traditional Search Improved
- Enter Semantic Search
- How It Works: Embeddings
- Comparing Meaning: Distance Metrics
- Why Similarity Alone Is Not Enough
- Modern Search = Hybrid Systems
- References & Further Reading
Module 2: First Principles of Vector Search
- What is a Vector?
- How Dimensions Represent Meaning
- Similarity Under the Hood
- Your First Qdrant Collection
- Points, Payloads, and Queries
Module 3: Sparse vs Dense vs Hybrid Search
- The Two Families of Search
- Hybrid Search: Dense + Sparse
- Setting Up Hybrid Search in Qdrant
- Fusion Strategies
- Beyond Text: Multimodal Search
Module 4: Designing a Vector Search System
- Architecture Layers of a Search System
- Filtering and Metadata Strategies
- Retrieval-Augmented Generation (RAG) Patterns
- Deployment Considerations
Module 5: Capstone - Multimodal Supplier Risk Intelligence
- Ingesting Multimodal Supplier Signals
- Clustering Signals Across Languages
- Querying the Capstone System
- Putting It All Together
Bonus Module: Further Reading
- Score Boosting
- Relevance Feedback
- Maximal Marginal Relevance (MMR)
- Re-ranking
- Other Advanced Techniques
Who It’s For
Anyone new to vector search who wants to understand the fundamentals. No prior experience with Qdrant or vector search engines required.
Time Commitment
- Core course (Modules 0-4): under 2 hours
- Capstone project (Module 5): ~3 hours
- Total: under 5 hours
- Bonus module: optional, not included in the total above
- Self-paced, flexible schedule
Ready to start your vector search journey?
What you’ll get
- Understand the fundamentals of vector search
- Learn why semantic search outperforms keyword search
- Build your first Qdrant collection
- Foundation for advanced courses