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BeginnercourseBootcamp access

AI Semantics Guide

56

Lessons

8

Modules

🎓

Bootcamp access

Lo que aprenderás

✓Explain what vectors are geometrically (direction, magnitude, high dimensions) without formal math
✓Understand vector spaces as "universes of meaning" where proximity equals similarity
✓Calculate cosine similarity conceptually and know when to use each distance metric
✓Design k-Nearest Neighbors search on paper and understand index trade-offs (HNSW, IVF)
✓Compare keyword search vs semantic search with a practical decision matrix
✓Diagram semantic search architectures (indexing pipeline, query pipeline, hybrid search)
✓Explain why RAG works and why retrieval quality determines RAG quality
✓Evaluate real-world use cases: Q&A systems, document search, code search, recommendations

¿Para quién es?

  • •AI Engineers who want deep conceptual understanding before implementing embeddings and RAG
  • •Developers who use vector databases and embeddings but don't fully understand the theory behind them
  • •Technical professionals transitioning into AI who need the mathematical intuition without the PhD
  • •Students in the AI Engineering Path between fundamentals and hands-on embedding implementation
  • •Anyone who wants to explain "how semantic search works" to their team with confidence

Requisitos

  • •AI Fundamentals Guide completed (what is an LLM, what is a transformer)
  • •Basic math: addition, subtraction, multiplication, square roots
  • •Basic geometry: Cartesian coordinates (x, y), 2D plane
  • •No linear algebra, no code, no ML experience required

Course content

1Módulo 1: Introducción a Vectores7 lessons
2Módulo 2: Espacios Vectoriales7 lessons
3Módulo 3: Similaridad y Distancia7 lessons
4Módulo 4: Búsqueda por Proximidad7 lessons
5Módulo 5: Keyword vs Semantic Search7 lessons
6Módulo 6: Diseño de Sistemas de Búsqueda8 lessons
7Módulo 7: RAG y Semantic Search7 lessons
8Módulo 8: Proyecto Final Integrador6 lessons
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