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Abstract cover artwork for RAG Systems in Production, a Agentic AI course Agentic AI
Agentic AI intermediate

RAG Systems in Production

The gap between a RAG demo and a RAG product is chunking strategy, retrieval evaluation and latency budgets. Covers document parsing, chunk boundary design, hybrid retrieval, cross-encoder re-ranking, and caching layers that keep p95 sane.

DO Daniel Okafor 4.9

$89.00

13 hours

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Abstract cover artwork for Recommender Systems from Scratch, a Machine Learning course Machine Learning
Machine Learning intermediate

Recommender Systems from Scratch

Build a recommender end to end: implicit-feedback matrix factorisation, content-based retrieval with embeddings, two-stage retrieve-then-rank architectures, and the cold-start problem every real system faces. Evaluated with offline ranking metrics and an honest discussion of why they disagree with A/B tests.

HT Hiroshi Tanaka 4.9

$89.00

15 hours

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Abstract cover artwork for Agentic RAG: Retrieval That Reasons, a Agentic AI course Agentic AI
Agentic AI intermediate

Agentic RAG: Retrieval That Reasons

Naive RAG retrieves once and hopes. Agentic RAG grades what it retrieved, rewrites the query when results are thin, and decides when it has enough context. Covers relevance grading, query rewriting loops, hybrid dense plus keyword retrieval, reciprocal rank fusion and LLM-as-judge re-ranking, with honest evaluation of when each technique actually helps.

DO Daniel Okafor 4.8

$79.00

10 hours

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