What You'll Learn

18 Chapters · 18 Lessons

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      Introduction to Machine Learning and AI-Assisted Coding

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      Getting Started with Prompt Engineering

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      Data Wrangling and Preprocessing

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      Classification and Regression

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      Clustering and Dimensionality

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      Evaluation Metrics and Model Validation

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      Using ChatGPT for Prompt Engineering in ML

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      GitHub Copilot and Code Interpreter in Practice

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      AI-Enhanced Feature Engineering and Selection

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      AI-Assisted Model Tuning and Hyperparameter Optimization

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      Building an Explainable AI

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      Regression and Resource Efficiency

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      Clustering and Customer Segmentation

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      Responsible AI and Model Integrity

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      Becoming an AI-Empowered ML Practitioner

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      Future Trends and Best Practices of AI-Driven Scikit-learn

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      Index

About the Course

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! 

About the Author

Bill Chen is a machine learning engineer, researcher, and technical author who builds practical ML systems from experimentation through deployment. His work spans scikit-learn, deep learning, evaluation, feature engineering, and production workflows, with a focus on turning AI-assisted coding into reliable, reproducible practice.