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Indigo Prompting Scikit-Learn for Machine Learning: Build Classification, Regression, Clustering, Feature Engineering, and Production ML Pipelines Faster with Scikit-Learn and AI Prompt Engineering (English Edition) | Erin Mills Town Centre
Prompting Scikit-Learn for Machine Learning: Build Classification, Regression, Clustering, Feature Engineering, and Production ML Pipelines Faster with Scikit-Learn and AI Prompt Engineering (English Edition)
Prompting Scikit-Learn for Machine Learning: Build Classification, Regression, Clustering, Feature Engineering, and Production ML Pipelines Faster with Scikit-Learn and AI Prompt Engineering (English Edition)
Prompting Scikit-Learn for Machine Learning: Build Classification, Regression, Clustering, Feature Engineering, and Production ML Pipelines Faster with Scikit-Learn and AI Prompt Engineering (English Edition)
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Machine Learning (ML) practitioners who know how to direct AI with statistical discipline are the ones building reliable production pipelines, while everyone else is still guessing and debugging. ***Prompting Scikit-Learn for Machine Learning***shows you how to translate natural-language intent directly into rigorous, reproducible ML workflows using AI, accelerating every stage from problem framing and data preparation to model deployment and drift management.
Rather than treating AI copilots as magic, this book puts disciplined AI-assisted execution at the centre. You use prompt engineering techniques with ChatGPT and GitHub Copilot to build leak-safe preprocessing pipelines, train classification, regression, clustering, and ensemble models, engineer features, interpret results, and validate every output with proper statistical rigour throughout.
Thus, by the end of this book, you will use AI prompts as a core part of your scikit-learn workflow, building and shipping production ML systems with reproducibility, explainability, and confidence!
Machine Learning (ML) practitioners who know how to direct AI with statistical discipline are the ones building reliable production pipelines, while everyone else is still guessing and debugging. ***Prompting Scikit-Learn for Machine Learning***shows you how to translate natural-language intent directly into rigorous, reproducible ML workflows using AI, accelerating every stage from problem framing and data preparation to model deployment and drift management.
Rather than treating AI copilots as magic, this book puts disciplined AI-assisted execution at the centre. You use prompt engineering techniques with ChatGPT and GitHub Copilot to build leak-safe preprocessing pipelines, train classification, regression, clustering, and ensemble models, engineer features, interpret results, and validate every output with proper statistical rigour throughout.
Thus, by the end of this book, you will use AI prompts as a core part of your scikit-learn workflow, building and shipping production ML systems with reproducibility, explainability, and confidence!
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