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Natural Language Processing (NLP)

Semantic Search and Sentence Embeddings

Sentence embeddings, vector databases and evaluating retrieval — the foundation of modern search and RAG.

Level
Intermediate
Lessons
3
Total time
46 min
Simulations
3
Included in Natural Language Processing (NLP) · $9
Tools you'll use
  • Python
  • PyTorch
Skills
  • Sentence embeddings
  • Vector databases
  • Hybrid search
Try it now — free

This is the real simulation from the module. No account needed.

What you will be able to do

  • Build a semantic search engine
  • Measure retrieval quality with recall@k

Before you start

A suggestion, never a lock — you can start here.

  • transformers-for-nlp
Modules that help first

Syllabus

3 lessons · 46 min

Every lesson's title and summary is public. The lessons themselves open once you own the track.

Every lesson in Natural Language Processing (NLP)

  1. Sentence Embeddings

    14 min

    From word vectors to sentence vectors, and models trained for similarity.

    Included with the track Simulation

  2. Vector Databases

    15 min

    Fast nearest-neighbour search, indexes and metadata filters.

    Included with the track Simulation

  3. Hybrid Search and Evaluation

    17 min

    Combining keywords and meaning, then measuring the result.

    Included with the track Simulation
Lifetime access
$9
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