---
title: Qdrant Essentials Course
page_title: Qdrant Essentials Course
description: The ultimate guide to production-grade vector search is here. And it’s free.
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  title: Qdrant Essentials Course
  description: The ultimate guide to production-grade vector search is here. And it’s free.
partition: course
---

# Qdrant Essentials Course

The ultimate guide to production-grade vector search is here. And it’s free.

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.

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

- Vector search fundamentals
- Performance optimization
- Hybrid and similarity search
- Portfolio project development

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## What Is the Course?

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.

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. 

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.

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.

## Course Overview

{{< accordion >}}
- title: "Days 0: Setup, Orientation & “Hello Qdrant!”"
  content: |
    - Welcome & Course Orientation
    - Environment Setup
    - Mini “Hello Qdrant!” Demo
  
- title: "Day 1: Core Qdrant Data Model & Vector Search 101"
  content: Content

- title: "Days 2: Indexing & Vector Storage Architecture"
  content: Content

- title: "Day 3: Hybrid Search"
  content: Content

- title: "Day 4: Optimizations & Query APIs"
  content: Content
{{< /accordion >}}

## Certificate of Completion

image

## Who Is the Course For?

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.

If your job title includes:

- Machine Learning Engineer
- Backend Developer
- Data Engineer
- Search Engineer
- MLOps Engineer

you’re in the right spot.

## Pre-Reqs

You don’t need prior Qdrant or vector database experience, but you should be comfortable with:
- Basic Python programming
- Running commands in your terminal
- Working with APIs or Python SDKs
- Some ML background (e.g., embeddings)

Optional but helpful:
- Docker basics
- Experience with search systems or deploying applications

{{< course-card 
title="Why Start Today"
image="/icons/outline/rocket-white-light.svg" 
link="/course/day-0/">}}

- Seeing practical examples (e.g., hybrid search, sparse+dense vectors)
- Learning key deployment tactics (multi-node clusters, on-disk indexing, RBAC)
- Building a final portfolio-grade project to showcase

{{< /course-card >}}