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A practical architecture guide to moving from standalone LLMs to RAG and agentic RAG systems with retrieval, planning, tool use, evaluation, and governance controls.
A practical guide to building agents and multi-agent systems in Python, including environments, goal-oriented behavior, communication, cooperation, BDI agents, learning agents, and real-world automation patterns.
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A practical architecture guide to building real-time GraphRAG applications with LangChain, Neo4j, GPT, document ingestion, graph extraction, and natural language querying.
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A practical guide to evaluating classification models in Python using confusion matrices, accuracy, precision, recall, F1, ROC-AUC, PR curves, calibration, cross-validation, and cost-sensitive metrics.
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A practical guide to the machine learning model lifecycle, covering scoping, problem definition, data preparation, EDA, feature engineering, model training, evaluation, deployment, monitoring, and maintenance.
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A practical guide to designing production machine learning systems, covering problem framing, business metrics, data pipelines, features, model selection, deployment, monitoring, and real-world ML use cases.
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Bhanu Pratap Singh