# Qdrant Essentials Course # Qdrant Essentials **Ship a production-ready docs search in 7 days** Build the vector search skills that matter: hybrid retrieval, multivector reranking, quantization, distributed deployment, and multitenancy. Ship a complete documentation search engine as your final project.

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7 days of lessons
Short, focused videos with hands‑on exercises
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Shareable certificate
Earn a digital certificate upon completion
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Flexible schedule
Learn at your own pace (1–2 hours/day)
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Beginner level
No prior Qdrant experience required

## What you'll learn
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### The Path **Days 0-2**: Foundations. Connect to Qdrant Cloud, work with points and payloads, compute semantic similarity, chunk text, and tune HNSW for speed and recall. **Days 3-5**: Advanced retrieval. Combine dense and sparse signals, do hybrid search with server-side fusion, use multivectors (ColBERT) with the Universal Query API, and build recommendations. **Day 6**: Ship. Wire ingestion, hybrid retrieval, multivector re-ranking, and evaluation (Recall@10, MRR, latency P50/P95). **Day 7 (bonus)**: Ecosystem. Try integrations with AI frameworks, search tools, and data pipelines. ## How the course works
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Video-first lessons
Clear, concise modules by the Qdrant team
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Final project
Ship a production-ready vector search app
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Bonus day
Explore partner integrations on Day 7
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Pitstop projects
Small builds each day to apply the concept

## Syllabus
Day 0: Setup and First Steps
  • Qdrant Cloud Setup
  • Implementing a Basic Vector Search
  • Project: Building Your First Vector Search System

→ Start Day 0

Day 1: Vector Search Fundamentals
  • Points, Vectors and Payloads
  • Distance Metrics
  • Text Chunking Strategies
  • Demo: Semantic Movie Search
  • Project: Building a Semantic Search Engine

→ Start Day 1

Day 2: Indexing and Performance
  • HNSW Indexing Fundamentals
  • Combining Vector Search and Filtering
  • Demo: HNSW Performance Tuning
  • Project: HNSW Performance Benchmarking

→ Start Day 2

Day 3: Hybrid Search
  • Sparse Vectors and Inverted Indexes
  • Demo: Keyword Search with Sparse Vectors
  • Hybrid Search with Score Fusion
  • Demo: Implementing a Hybrid Search System
  • Project: Building a Hybrid Search Engine

→ Start Day 3

Day 4: Optimization and Scale
  • Vector Quantization Methods
  • Accuracy Recovery with Rescoring
  • High-Throughput Data Ingestion
  • Project: Quantization Performance Optimization

→ Start Day 4

Day 5: Advanced APIs
  • Multivectors for Late Interaction Models
  • The Universal Query API
  • Demo: Universal Query for Hybrid Retrieval
  • Project: Building a Recommendation System

→ Start Day 5

Day 6: Final Project - Building a Production-Grade Search Engine
  • Project Architecture and Evaluation Framework
  • Implementation and Performance Evaluation
  • Course Summary and Next Steps

→ Start Day 6

Day 7: Partner Ecosystem Integrations (Bonus)
  • AI & LLM Frameworks (Haystack, Jina AI, TwelveLabs)
  • Data Processing (Unstructured.io)
  • ML Platforms & Analytics (Tensorlake, Vectorize.io, Superlinked, Quotient)

→ Start Day 7

## Who it's for ML, backend, data, and search engineers building RAG, semantic search, or recommendations. Requires intermediate Python, basic CLI/APIs, and familiarity with embeddings. ## Time commitment - Duration: 6 days at 1-2 hours/day + 1 optional bonus day - Video learning: ~3 hours - Hands-on learning: 4-5 hours - Final project: 2-4 hours - Total: 9-12 hours
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What you’ll get

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