Prompt Smarter. Model Better. Deploy with Confidence.
Key Features
? Get a free one-month digital subscription to www.avaskillshelf.com.
? Complete Scikit-learn ML workflow from data preprocessing and feature engineering to production deployment.
? Hands-on AI-assisted development using ChatGPT and GitHub Copilot for faster coding and debugging.
? Production ML engineering with leak-safe pipelines, explainability, drift management, and responsible AI.
Book Description
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.
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.
? Use ChatGPT and GitHub Copilot to plan code, debug, and optimize ML workflows faster.
Table of Contents
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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
Index
