A comprehensive, hands-on guide to mastering scikit-learn — from setup to production-ready machine learning pipelines, with real-world examples, pitfalls, and best practices.
A deep dive into model evaluation metrics — how to choose, implement, and interpret them for real-world machine learning systems.
A deep dive into detecting and mitigating bias in AI systems — from understanding fairness metrics to implementing bias audits with Python. Learn how companies tackle ethical AI challenges at scale.
A deep dive into cross-validation techniques — from k-fold to stratified and time-series CV — with practical examples, pitfalls, and production insights.
Learn how to make large language model outputs consistent and reliable using structured prompts, temperature control, and Pydantic validation.
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