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behavioural recommendation agent

It watches how you learn,
then argues for what's next.

Every click, search and second of attention feeds a seven-node agent that writes a recommendation citing the evidence — and you can open the reasoning.

Create an account Browse the catalog How the agent works →
  • 7-node graph
  • dense ⊕ bm25
  • rrf fusion
  • llm judge
  • mesh gateway

◈ Nexora

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The highest-rated courses across every category.

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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 FastAPI in Production, a Python course Python
Python intermediate

FastAPI in Production

Dependency injection that stays testable, Pydantic models as your contract, SQLAlchemy 2.0 sessions scoped correctly, background tasks that do not block responses, structured logging, and graceful shutdown. Ends with a service that has readiness probes, migrations and a real test suite.

TN Tomas Nowak 4.9

$69.00

12 hours

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Abstract cover artwork for Transformers and Attention, Implemented Line by Line, a Deep Learning course Deep Learning
Deep Learning advanced

Transformers and Attention, Implemented Line by Line

Build a transformer from scratch: scaled dot-product attention, multi-head projections, positional encodings, KV caching and the modern variants (RoPE, grouped-query attention, RMSNorm) that make inference cheap. You will train a small model, profile it, and understand precisely where the FLOPs go.

HT Hiroshi Tanaka 4.9

$109.00

17 hours

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Abstract cover artwork for Deep Learning with PyTorch: Fundamentals to Fine-Tuning, a Deep Learning course Deep Learning
Deep Learning intermediate

Deep Learning with PyTorch: Fundamentals to Fine-Tuning

Autograd from first principles, then real training loops: learning-rate schedules, mixed precision, gradient accumulation, distributed data parallel, and the debugging discipline for a loss that will not go down. Finishes by fine-tuning a pretrained model on a custom dataset with a proper evaluation harness.

HT Hiroshi Tanaka 4.9

$99.00

22 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 Building Production Agents with LangGraph, a Agentic AI course Agentic AI
Agentic AI intermediate

Building Production Agents with LangGraph

Design agents as explicit state machines instead of prompt spaghetti. You will build a typed graph with conditional edges, add checkpointing so runs are replayable, implement retry and refinement loops, and ship an agent that degrades gracefully when a model call fails. Ends with a production deployment that handles concurrency, idempotency and observability.

PR Priya Raghavan 4.9

$89.00

14 hours

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Agentic AI Career Skills Cloud Data Engineering Deep Learning DevOps JavaScript Machine Learning Python Web Development