landing_page/qdrant-landing/content/course/essentials/_index.md

---
title: Qdrant Essentials Course
page_title: Qdrant Essentials Course
description: Learn hybrid search, multivectors, and production deployment in 7 days. Build and ship a docs search engine.
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  sidebarTitle: Qdrant Essentials
  menuTitle:
    text: Course Overview
    url: /course/essentials/
  nextButton: Continue to Next Video
  nextDay: Complete
  title: Qdrant Essentials
  description: Learn hybrid search, multivectors, and production deployment in 7 days. Build and ship a docs search engine.
partition: 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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{{< cards-list >}}
- icon: /icons/outline/play-white.svg
  title: 7 days of lessons
  content: Short, focused videos with hands‑on exercises
- icon: /icons/outline/cloud-check-blue.svg
  title: Shareable certificate
  content: Earn a digital certificate upon completion
- icon: /icons/outline/time-blue.svg
  title: Flexible schedule
  content: Learn at your own pace (1–2 hours/day)
- icon: /icons/outline/plan.svg
  title: Beginner level
  content: No prior Qdrant experience required
  
{{< /cards-list >}}

<br/>

## What you'll learn
{{< course-card
 title="Skills you'll gain:"
 image="/icons/outline/training-white.svg"
 type="wide-list">}}

- Qdrant data modeling: points, payloads, and schemas
- Embeddings, chunking, and similarity metrics
- Indexing and retrieval tuning ([HNSW](https://qdrant.tech/articles/filtrable-hnsw/), filters, recall/latency)
- Hybrid search with sparse + dense vectors and re-ranking
- Performance optimization, compression, and quantization
- Scaling, sharding/replication, and security

{{< /course-card >}}

### 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

{{< cards-list >}}

- icon: /icons/outline/training-purple.svg
  title: Video-first lessons
  content: Clear, concise modules by the Qdrant team
- icon: /icons/outline/hacker-purple.svg
  title: Final project
  content: Ship a production-ready vector search app
- icon: /icons/outline/similarity-blue.svg
  title: Bonus day
  content: Explore partner integrations on Day 7
- icon: /icons/outline/copy.svg
  title: Pitstop projects
  content: Small builds each day to apply the concept
  {{< /cards-list >}}

<br/>

## Syllabus

{{< accordion >}}
- title: "Day 0: Setup and First Steps"
  content: |
    - Qdrant Cloud Setup
    - Implementing a Basic Vector Search
    - Project: Building Your First Vector Search System
    <br>
    <br>
    <p style="margin-left: 0px;"><a href="/course/essentials/day-0/">→ Start Day 0</a></p>

- title: "Day 1: Vector Search Fundamentals"
  content: |
    - Points, Vectors and Payloads
    - Distance Metrics
    - Text Chunking Strategies
    - Demo: Semantic Movie Search
    - Project: Building a Semantic Search Engine
    <br>
    <br>
    <p style="margin-left: 0px;"><a href="/course/essentials/day-1/">→ Start Day 1</a></p>

- title: "Day 2: Indexing and Performance"
  content: |
    - HNSW Indexing Fundamentals
    - Combining Vector Search and Filtering
    - Demo: HNSW Performance Tuning
    - Project: HNSW Performance Benchmarking
    <br>
    <br>
    <p style="margin-left: 0px;"><a href="/course/essentials/day-2/">→ Start Day 2</a></p>

- title: "Day 3: Hybrid Search"
  content: |
    - 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
    <br>
    <br>
    <p style="margin-left: 0px;"><a href="/course/essentials/day-3/">→ Start Day 3</a></p>

- title: "Day 4: Optimization and Scale"
  content: |
    - Vector Quantization Methods
    - Accuracy Recovery with Rescoring
    - High-Throughput Data Ingestion
    - Project: Quantization Performance Optimization
    <br>
    <br>
    <p style="margin-left: 0px;"><a href="/course/essentials/day-4/">→ Start Day 4</a></p>

- title: "Day 5: Advanced APIs"
  content: |
    - Multivectors for Late Interaction Models
    - The Universal Query API
    - Demo: Universal Query for Hybrid Retrieval
    - Project: Building a Recommendation System
    <br>
    <br>
    <p style="margin-left: 0px;"><a href="/course/essentials/day-5/">→ Start Day 5</a></p>

- title: "Day 6: Final Project - Building a Production-Grade Search Engine"
  content: |
    - Project Architecture and Evaluation Framework
    - Implementation and Performance Evaluation
    - Course Summary and Next Steps
    <br>
    <br>
    <p style="margin-left: 0px;"><a href="/course/essentials/day-6/">→ Start Day 6</a></p>

- title: "Day 7: Partner Ecosystem Integrations (Bonus)"
  content: |
    - AI & LLM Frameworks (Haystack, Jina AI, TwelveLabs)
    - Data Processing (Unstructured.io)
    - ML Platforms & Analytics (Tensorlake, Vectorize.io, Superlinked, Quotient)
    <br>
    <br>
    <p style="margin-left: 0px;"><a href="/course/essentials/day-7/">→ Start day 7</a></p>
{{< /accordion >}}


## 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: 7 days at 1–2 hours/day + optional bonus day
- Video learning: ~3 hours
- Hands-on learning: 4-5 hours
- Final project: 2–4 hours
- Total: 9–12 hours


{{< course-card 
 title="Ready to start your vector search journey?"
 image="/icons/outline/rocket-white-light.svg" 
 link="/course/essentials/day-0/">}}
**What you’ll get**
- Build a production-ready docs search engine
- Practice with real projects
- Learn performance tuning techniques
- Portfolio artifacts and community support
{{< /course-card >}}