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BásicocourseAcceso por bootcamp
AI Semantics Guide
56
Lecciones
8
Módulos
🎓
Acceso por bootcamp
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
Contenido del curso
1Módulo 1: Introducción a Vectores7 lecciones
2Módulo 2: Espacios Vectoriales7 lecciones
3Módulo 3: Similaridad y Distancia7 lecciones
4Módulo 4: Búsqueda por Proximidad7 lecciones
5Módulo 5: Keyword vs Semantic Search7 lecciones
6Módulo 6: Diseño de Sistemas de Búsqueda8 lecciones
7Módulo 7: RAG y Semantic Search7 lecciones
8Módulo 8: Proyecto Final Integrador6 lecciones
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