Beginners 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.
Multiple modules
Focused lessons building from fundamentals to practical applications
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 keyword search breaks and how semantic search solves it
- How embeddings convert text to vectors that capture meaning
- Distance metrics: cosine similarity, dot product, and Euclidean
- Hybrid search: combining dense and sparse retrieval
- Building your first Qdrant collection and queries
The Path
Module 0: Setup. 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. Learn what vectors are, how dimensions represent meaning, similarity metrics, and build your first Qdrant collection.
How the course works
Clear lessons
Focused modules by the Qdrant team
Hands-on learning
Practical examples and exercises
Progressive learning
Build from fundamentals to advanced concepts
Self-paced
Learn at your own speedSyllabus
Module 0: Setting Up Dependencies
- Qdrant Cloud Setup
- Implementing a Basic Vector Search
- Project: Building Your First Vector Search System
Module 1: Let's Understand Search
- The Problem: Why Keyword Search Breaks
- 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
→ Coming soon
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
→ Coming soon
Module 3: Sparse vs Dense vs Hybrid Search
- The Two Families of Search
- Hybrid Search: Dense + Sparse + Filters
- Setting Up Hybrid Search in Qdrant
- Fusion Strategies
- Beyond Text: Multimodal Search
- Real-World Use Cases
→ Coming soon
Who it’s for
Anyone new to vector search who wants to understand the fundamentals. No prior experience with Qdrant or vector databases required.
Time commitment
- Duration: Multiple modules
- Self-paced learning
- 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