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BeginnercourseBootcamp access

Embeddings Deep Dive Guide

64

Lessons

8

Modules

🎓

Bootcamp access

Lo que aprenderás

✓Understand embeddings architecture: transformers, tokenization, pooling, normalization
✓Compare embedding models objectively: OpenAI, SBERT, BGE with MTEB benchmarks
✓Implement semantic search from scratch with numpy (no black-box libraries)
✓Apply chunking strategies: fixed-size, semantic, recursive — and evaluate which works best
✓Master distance metrics: cosine similarity, euclidean, dot product — know when to use each
✓Perform embedding operations: arithmetic, interpolation, composition, clustering
✓Optimize for production: caching, error handling, monitoring, scaling, cost control
✓Evaluate embedding quality with golden datasets, precision@k, recall@k, MRR
✓Build a complete semantic search engine with FAISS, query expansion and reranking

¿Para quién es?

  • •AI Engineers implementing semantic search or RAG who need deep embedding understanding
  • •Backend developers transitioning to AI who want production-ready skills from day one
  • •Professionals who use `langchain.embeddings` but can't debug when things break
  • •Teams choosing embedding models who need an objective comparison framework
  • •Students in the AI Engineering Path preparing for vector databases and advanced RAG

Requisitos

  • •Python basics + OOP (loops, functions, classes)
  • •REST APIs and HTTP fundamentals (requests, JSON)
  • •OpenAI API access (or open-source alternative)
  • •Vector concepts from AI Semantics Guide (#5) — what vectors are, cosine similarity, semantic search concept
  • •Basic numpy (arrays, operations)

Course content

1Módulo 1: ¿Qué son Embeddings?8 lessons
2Módulo 2: ¿Cómo funcionan Embeddings?8 lessons
3Módulo 3: Modelos de Embeddings (Comparación)8 lessons
4Módulo 4: Evaluación y Chunking Strategies8 lessons
5Módulo 5: Distance Metrics Profundo8 lessons
6Módulo 6: Embedding Operations8 lessons
7Módulo 7: Production Patterns8 lessons
8Módulo 8: Proyecto Integrador8 lessons
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