Machine Learning for Data Diagnostics: From Raw Data to Local LLM Agents

Built to make you work, not listen: five-minute lessons, a quiz after each one, guided exercises on one dataset of 40 industrial machines. From pandas, SQL and feature engineering to regression, random forests, clustering and neural networks; then embeddings, RAG, local LLMs with Ollama and LangChain, text-to-SQL tools, LangGraph agents and a Streamlit diagnostic app. Free, offline, no API key.

IntermediateAOA-MLD-201
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Week 1 — AI and LLM foundations

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Week 2 — Python data retrieval and exploration

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Week 3 — Data sources and database types

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Week 4 — Data preparation and dataset separation

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Week 5 — Regression, classification and evaluation

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Week 6 — Model selection and validation

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Week 7 — Clustering and neural networks

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Week 8 — NLP and text representations

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Week 9 — Retrieval and vector databases

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Week 10 — Local LLMs and LangChain

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Week 11 — LLM tools and database querying

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Week 12 — Stateful workflows with LangGraph

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Week 13 — Observability and model reliability

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Week 14 — Diagnostic application integration

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Week 15 — Final project and presentation

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