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Vector Databases & Semantic Search

Unlock semantic understanding by mastering vector databases and efficient similarity search.

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(7,800)
7,800 studentsEnglish

Created by Dr. Ivan Petrov · ML Systems Engineer

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This course includes

  • 15 video lessons
  • 3h 55m of content
  • Downloadable resources
  • Full lifetime access
  • Certificate of completion
  • 30-day money-back guarantee

What you'll learn

  • Understand the principles of vector embeddings and semantic similarity.
  • Generate embeddings using popular libraries and models.
  • Compare and contrast leading vector databases (Pinecone, Weaviate, ChromaDB, Qdrant).
  • Implement efficient indexing strategies for vector data.
  • Perform similarity searches using ANN algorithms.
  • Apply filtering and metadata to refine search results.
  • Integrate vector search into RAG and recommendation systems.
  • Evaluate the performance of vector search implementations.

About this course

Traditional databases struggle with unstructured data. This course introduces the power of vector databases, enabling you to perform semantic search and build intelligent applications that understand meaning, not just keywords. You'll learn how vector embeddings capture the essence of text, images, and other data types, and how databases like Pinecone, Weaviate, and ChromaDB store and query these vectors efficiently. We'll cover the entire pipeline: from generating embeddings using models like Sentence-BERT or OpenAI's Ada, to indexing strategies within various vector databases, and finally, implementing sophisticated similarity search queries. Understand concepts like Approximate Nearest Neighbor (ANN) search, filtering, and hybrid search to retrieve the most relevant results for your specific use case. Gain practical experience building applications that leverage these capabilities. Whether you're enhancing search functionality, implementing recommendation systems, or powering RAG pipelines for LLMs, mastering vector databases is crucial. This course provides the foundational knowledge and hands-on skills to effectively utilize these cutting-edge technologies.

Course content

5 modules · 15 lessons · 3h 55m

  • From Words to Vectors: An IntroductionPreview
    15m
  • Semantic Similarity ExplainedPreview
    12m
  • Generating Embeddings with Sentence Transformers
    18m

Requirements

  • Basic Python programming knowledge.
  • Familiarity with data structures and algorithms.
  • Conceptual understanding of machine learning is helpful.

Your instructor

D

Dr. Ivan Petrov

ML Systems Engineer

Ivan builds LLM infrastructure serving millions of users and has the production scars to prove it.