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title, page_title, short_description, description, content, partition

title	page_title	short_description	description	content	partition
Beginner Course	Qdrant Beginner Course	Learn the fundamentals of vector search: why keyword search struggles, how semantic search improves it, embeddings, distance metrics, and hybrid systems.	Understand why traditional search struggles and how modern semantic search improves it, and build your first search system.	sidebarTitle menuTitle nextButton nextDay title description Beginner Course text url Course Overview /course/beginners/ Continue to Next Step Complete Beginner Course Understand why traditional search struggles and how modern semantic search improves it, and build your first search system.	course

Beginner 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.

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• icon: /icons/outline/play-white.svg title: 6 modules content: From setting up dependencies to a hands-on capstone project
• 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
• icon: /icons/outline/plan.svg title: Beginner level content: No prior experience required

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What You'll Learn

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

• Why traditional search struggles and how modern semantic search improves it
• How embeddings convert text to vectors that capture meaning
• Distance metrics: cosine similarity, dot product, Euclidean and Manhattan
• Hybrid search: combining dense and sparse retrieval
• Building your first Qdrant collection and queries

{{< /course-card >}}

The Path

Module 0: Setting Up Dependencies. 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. Anatomy of a vector - how data is stored, indexed, and retrieved in Qdrant.

Module 3: Sparse vs Dense vs Hybrid Search. Understand dense vs sparse search, when each fails, and how hybrid systems combine them.

Module 4: Designing a Vector Search System. How to design a vector search system - layers, filtering, RAG, and deployment.

Module 5: Capstone - Multimodal Supplier Risk Intelligence. Ingest, cluster, and query multimodal supplier signals across languages.

Bonus Module: Further Reading. A roundup of advanced techniques for further reading: score boosting, relevance feedback, MMR, and re-ranking.

How the Course Works

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• icon: /icons/outline/training-purple.svg title: Bite-sized lessons content: Short, friendly modules you can finish in one sitting
• icon: /icons/outline/hacker-purple.svg title: Learn by doing content: Follow along with real examples and hands-on exercises
• icon: /icons/outline/similarity-blue.svg title: One step at a time content: Each module builds on the last, so nothing feels out of reach
• icon: /icons/outline/copy.svg title: Go at your own pace content: Pause anytime and pick up right where you left off {{< /cards-list >}}

Syllabus

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• title: "Module 0: Setting Up Dependencies" content: |

  • Qdrant Cloud Setup
  • Implementing a Basic Vector Search
    
    

  → Start Module 0

• title: "Module 1: Let's Understand Search" content: |

  • The Problem: Why Traditional Search Struggles
  • 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
    
    

  → Start Module 1

• title: "Module 2: First Principles of Vector Search" content: |

  • What is a Vector?
  • How Dimensions Represent Meaning
  • Similarity Under the Hood
  • Your First Qdrant Collection
  • Points, Payloads, and Queries
    
    

  → Start Module 2

• title: "Module 3: Sparse vs Dense vs Hybrid Search" content: |

  • The Two Families of Search
  • Hybrid Search: Dense + Sparse
  • Setting Up Hybrid Search in Qdrant
  • Fusion Strategies
  • Beyond Text: Multimodal Search
    
    

  → Start Module 3

• title: "Module 4: Designing a Vector Search System" content: |

  • Architecture Layers of a Search System
  • Filtering and Metadata Strategies
  • Retrieval-Augmented Generation (RAG) Patterns
  • Deployment Considerations
    
    

  → Start Module 4

• title: "Module 5: Capstone - Multimodal Supplier Risk Intelligence" content: |

  • Ingesting Multimodal Supplier Signals
  • Clustering Signals Across Languages
  • Querying the Capstone System
  • Putting It All Together
    
    

  → Start Module 5

• title: "Bonus Module: Further Reading" content: |

  • Score Boosting
  • Relevance Feedback
  • Maximal Marginal Relevance (MMR)
  • Re-ranking
  • Other Advanced Techniques
    
    

  → Start Module 6

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Who It's For

Anyone new to vector search who wants to understand the fundamentals. No prior experience with Qdrant or vector search engines required.

Time Commitment

• Core course (Modules 0-4): under 2 hours
• Capstone project (Module 5): ~3 hours
• Total: under 5 hours
• Bonus module: optional, not included in the total above
• Self-paced, flexible schedule

{{< course-card title="Ready to start your vector search journey?" image="/icons/outline/rocket-white-light.svg" link="/course/beginners/module-0/">}} 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 {{< /course-card >}}