Librería Samer Atenea
Kálamo Books
Librería Elías (Asturias)
Librería Kolima (Madrid)
Librería Proteo (Málaga)
Prompt Smarter. Model Better. Deploy with Confidence.Book DescriptionMachine 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.What you will learn● Frame business problems as ML tasks, and identify when machine learning is the right solution.● Build leak-safe preprocessing pipelines with proper splits, encodings, and feature engineering.● Train and evaluate classification, regression, clustering, and ensemble models using scikit-learn.Table of Contents1. Introduction to Machine Learning and AI-Assisted Coding2. Getting Started with Prompt Engineering3. Data Wrangling and Preprocessing4. Classification and Regression5. Clustering and Dimensionality6. Evaluation Metrics and Model Validation7. Using ChatGPT for Prompt Engineering in ML8. GitHub Copilot and Code Interpreter in Practice9. AI-Enhanced Feature Engineering and Selection10. AI-Assisted Model Tuning and Hyperparameter Optimization11. Building an Explainable AI12. Regression and Resource Efficiency13. Clustering and Customer Segmentation14. Responsible AI and Model Integrity15. Becoming an AI-Empowered ML Practitioner16. Future Trends and Best Practices of AI-Driven Scikit-learn Index
