Prompting Scikit-Learn for Machine Learning

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.

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!

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.
● Interpret and explain model decisions using explainability techniques for stakeholder communication.
● Deploy, monitor, and retrain production ML models with drift management and reproducibility built in.

1. Introduction to Machine Learning and AI-Assisted Coding
2. Getting Started with Prompt Engineering
3. Data Wrangling and Preprocessing
4. Classification and Regression
5. Clustering and Dimensionality
6. Evaluation Metrics and Model Validation
7. Using ChatGPT for Prompt Engineering in ML
8. GitHub Copilot and Code Interpreter in Practice
9. AI-Enhanced Feature Engineering and Selection
10. AI-Assisted Model Tuning and Hyperparameter Optimization
11. Building an Explainable AI
12. Regression and Resource Efficiency
13. Clustering and Customer Segmentation
14. Responsible AI and Model Integrity
15. Becoming an AI-Empowered ML Practitioner
16. Future Trends and Best Practices of AI-Driven Scikit-learn
Index

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.

About the Technical Reviewer

Anandu K Balachandran has over five years of experience designing and deploying AI and Machine Learning (ML) systems, with work spanning autonomous agents, privacy-preserving methods, and end-to-end production pipelines. He has been involved across the full AI lifecycle—from research and patents to building scalable solutions on cloud platforms such as AWS and Azure—and has applied these skills in education technology, biotechnology, sustainable energy, and finance for organizations, including the Wadhwani Foundation, Zion-AI, and Accenture. He holds an M.S. in Data Science from the University of Arizona and an M. Tech from IIT Delhi.

Anandu is currently a Senior Data Science and AI Consultant at the Wadhwani Foundation, leading the development of agentic systems on AI for good initiatives. He has been recognized at Accenture with two Inspiring Innovator awards for patent and research contributions, and was named to the Dean’s List of Distinguished Scholars during his Master’s or Post-Graduation.

Deeply committed to technical rigor, he has mentored Columbia University Master's students on industry-collaborative reinforcement learning projects and possesses extensive expertise in validating and optimizing machine learning systems. He brings a balanced perspective that emphasizes technical soundness, clarity of exposition, and practical relevance in applied AI systems.