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docs: Add course content for overview page (#1938)
* docs: Add course content for overview page * added course video * propper youtube embedding link * removed getStarted button * wording * form test * took out day 6,7 * day 8 and 9 folders renamed to day-6 and day-7 * certification coming soon * dicscord on faq * dicscord on faq --------- Co-authored-by: Kirstin <kirstin.taufertshoefer@qdrant.com>
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Kirstin
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---
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title: Qdrant Essentials Course
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page_title: Qdrant Essentials Course
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description: The ultimate guide to production-grade vector search is here. And it’s free.
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description: Learn hybrid search, multivectors, and production deployment in 7 days. Build and ship a docs search engine.
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content:
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sidebarTitle: Qdrant Essentials
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menuTitle:
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text: Course Overview
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url: /course/essentials/
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getStarted:
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text: Get Started
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url: /course/essentials/day-0/
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nextButton: Continue to Next Video
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nextDay: Complete
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title: Qdrant Essentials Course
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description: The ultimate guide to production-grade vector search is here. And it’s free.
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title: Qdrant Essentials
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description: Learn hybrid search, multivectors, and production deployment in 7 days. Build and ship a docs search engine.
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partition: course
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---
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# Qdrant Essentials Course
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# Qdrant Essentials
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The ultimate guide to production-grade vector search is here. And it’s free.
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**Ship a production-ready docs search in 7 days**
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From your first vector upsert to optimizing high-performance retrieval at scale, this free course takes you from zero to production-ready. Learn how to build efficient vector search, fine-tune Qdrant for maximum performance, and keep your system lightweight, even when working with billions of vectors.
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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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<div class="video">
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<iframe
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src="https://www.youtube.com/embed/QnRjMolv8Qk?si=uqWQLcLp_oBWt3bO"
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frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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referrerpolicy="strict-origin-when-cross-origin"
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allowfullscreen>
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</iframe>
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</div>
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<br/>
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{{< cards-list >}}
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- icon: /icons/outline/play-white.svg
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title: 7 days of lessons
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content: Short, focused videos with hands‑on exercises
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- icon: /icons/outline/cloud-check-blue.svg
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title: Shareable certificate
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content: Earn a digital certificate upon completion
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- icon: /icons/outline/time-blue.svg
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title: Flexible schedule
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content: Learn at your own pace (1–2 hours/day)
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- icon: /icons/outline/plan.svg
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title: Beginner level
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content: No prior Qdrant experience required
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{{< /cards-list >}}
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<br/>
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## What you'll learn
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{{< course-card
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title="Skills you’ll gain:"
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image="/icons/outline/training-white.svg"
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isWideList="true">}}
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- Vector search fundamentals
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- Performance optimization
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- Hybrid and similarity search
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- Portfolio project development
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title="Skills you'll gain:"
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image="/icons/outline/training-white.svg"
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type="wide-list">}}
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- Qdrant data modeling: points, payloads, and schemas
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- Embeddings, chunking, and similarity metrics
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- Indexing and retrieval tuning ([HNSW](https://qdrant.tech/articles/filtrable-hnsw/), filters, recall/latency)
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- Hybrid search with sparse + dense vectors and re-ranking
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- Performance optimization, compression, and quantization
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- Scaling, sharding/replication, and security
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{{< /course-card >}}
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## What Is the Course?
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### The Path
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No matter if you're exploring vector search for the first time or fine-tuning a large-scale RAG system, this free course gives you the practical foundation and advanced skills you need.
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**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.
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Over 9 days (plus bonus content), you’ll build up from the fundamentals to advanced deployment strategies with Qdrant. Each module focuses on a single concept or capability, paired with a hands-on exercise to apply what you’ve learned. You will start with basics, build confidence, and gradually progress to complex topics.
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**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.
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Every day includes a hands-on exercise or mini-project, like creating a collection, uploading points, building a hybrid search pipeline, or tuning the HNSW index.
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**Day 6**: Ship. Wire ingestion, hybrid retrieval, multivector re-ranking, and evaluation (Recall@10, MRR, latency P50/P95).
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At the end, you’ll bring everything together by building a full production-grade vector search application. You’ll graduate with a portfolio-worthy project, plus a deep understanding of how to apply Qdrant in production scenarios.
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**Day 7 (bonus)**: Ecosystem. Try integrations with AI frameworks, search tools, and data pipelines.
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## Course Overview
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## How the course works
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{{< cards-list >}}
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- icon: /icons/outline/training-purple.svg
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title: Video-first lessons
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content: Clear, concise modules by the Qdrant team
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- icon: /icons/outline/hacker-purple.svg
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title: Final project
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content: Ship a production-ready vector search app
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- icon: /icons/outline/similarity-blue.svg
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title: Bonus day
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content: Explore partner integrations on Day 7
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- icon: /icons/outline/copy.svg
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title: Pitstop projects
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content: Small builds each day to apply the concept
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{{< /cards-list >}}
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<br/>
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## Syllabus
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{{< accordion >}}
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- title: "Days 0: Setup, Orientation & “Hello Qdrant!”"
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- title: "Day 0: Setup and First Steps"
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content: |
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- Welcome & Course Orientation
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- Environment Setup
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- Mini “Hello Qdrant!” Demo
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- title: "Day 1: Core Qdrant Data Model & Vector Search 101"
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content: Content
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- Qdrant Cloud Setup
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- Implementing a Basic Vector Search
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- Project: Building Your First Vector Search System
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<br>
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<br>
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<p style="margin-left: 0px;"><a href="/course/essentials/day-0/">→ Start Day 0</a></p>
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- title: "Days 2: Indexing & Vector Storage Architecture"
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content: Content
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- title: "Day 1: Vector Search Fundamentals"
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content: |
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- Points, Vectors and Payloads
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- Distance Metrics
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- Text Chunking Strategies
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- Demo: Semantic Movie Search
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- Project: Building a Semantic Search Engine
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<br>
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<br>
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<p style="margin-left: 0px;"><a href="/course/essentials/day-1/">→ Start Day 1</a></p>
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- title: "Day 2: Indexing and Performance"
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content: |
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- HNSW Indexing Fundamentals
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- Combining Vector Search and Filtering
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- Demo: HNSW Performance Tuning
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- Project: HNSW Performance Benchmarking
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<br>
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<br>
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<p style="margin-left: 0px;"><a href="/course/essentials/day-2/">→ Start Day 2</a></p>
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- title: "Day 3: Hybrid Search"
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content: Content
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content: |
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- Sparse Vectors and Inverted Indexes
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- Demo: Keyword Search with Sparse Vectors
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- Hybrid Search with Score Fusion
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- Demo: Implementing a Hybrid Search System
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- Project: Building a Hybrid Search Engine
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<br>
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<br>
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<p style="margin-left: 0px;"><a href="/course/essentials/day-3/">→ Start Day 3</a></p>
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- title: "Day 4: Optimizations & Query APIs"
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content: Content
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- title: "Day 4: Optimization and Scale"
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content: |
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- Vector Quantization Methods
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- Accuracy Recovery with Rescoring
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- High-Throughput Data Ingestion
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- Project: Quantization Performance Optimization
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<br>
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<br>
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<p style="margin-left: 0px;"><a href="/course/essentials/day-4/">→ Start Day 4</a></p>
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- title: "Day 5: Advanced APIs"
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content: |
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- Multivectors for Late Interaction Models
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- The Universal Query API
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- Demo: Universal Query for Hybrid Retrieval
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- Project: Building a Recommendation System
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<br>
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<br>
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<p style="margin-left: 0px;"><a href="/course/essentials/day-5/">→ Start Day 5</a></p>
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- title: "Day 6: Final Project - Building a Production-Grade Search Engine"
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content: |
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- Project Architecture and Evaluation Framework
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- Implementation and Performance Evaluation
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- Course Summary and Next Steps
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<br>
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<br>
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<p style="margin-left: 0px;"><a href="/course/essentials/day-6/">→ Start Day 6</a></p>
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- title: "Day 7: Partner Ecosystem Integrations (Bonus)"
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content: |
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- AI & LLM Frameworks (Haystack, Jina AI, TwelveLabs)
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- Data Processing (Unstructured.io)
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- ML Platforms & Analytics (Tensorlake, Vectorize.io, Superlinked, Quotient)
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<br>
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<br>
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<p style="margin-left: 0px;"><a href="/course/essentials/day-7/">→ Start day 7</a></p>
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{{< /accordion >}}
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## Certificate of Completion
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image
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## Who it's for
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## Who Is the Course For?
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ML, backend, data, and search engineers building RAG, semantic search, or recommendations. Requires intermediate Python, basic CLI/APIs, and familiarity with embeddings.
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You! But really, this course is great for hands-on professionals who need to build or improve applications with semantic or hybrid search capabilities, or developers exploring vector databases for the first time.
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## Time commitment
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If your job title includes:
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- Duration: 7 days at 1–2 hours/day + optional bonus day
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- Video learning: ~3 hours
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- Hands-on learning: 4-5 hours
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- Final project: 2–4 hours
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- Total: 9–12 hours
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- Machine Learning Engineer
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- Backend Developer
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- Data Engineer
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- Search Engineer
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- MLOps Engineer
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you’re in the right spot.
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## Pre-Reqs
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You don’t need prior Qdrant or vector database experience, but you should be comfortable with:
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- Basic Python programming
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- Running commands in your terminal
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- Working with APIs or Python SDKs
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- Some ML background (e.g., embeddings)
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Optional but helpful:
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- Docker basics
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- Experience with search systems or deploying applications
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{{< course-card
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title="Why Start Today"
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image="/icons/outline/rocket-white-light.svg"
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link="/course/day-0/">}}
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- Seeing practical examples (e.g., hybrid search, sparse+dense vectors)
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- Learning key deployment tactics (multi-node clusters, on-disk indexing, RBAC)
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- Building a final portfolio-grade project to showcase
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{{< /course-card >}}
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title="Ready to start your vector search journey?"
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image="/icons/outline/rocket-white-light.svg"
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link="/course/essentials/day-0/">}}
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**What you’ll get**
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- Build a production-ready docs search engine
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- Practice with real projects
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- Learn performance tuning techniques
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- Portfolio artifacts and community support
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{{< /course-card >}}
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