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Coles Prompting Scikit-Learn for Machine Learning at Bayshore Shopping Centre in Ottawa, ON
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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
Introduction to Machine Learning and AI-Assisted Coding
Getting Started with Prompt Engineering
Data Wrangling and Preprocessing
Classification and Regression
Clustering and Dimensionality
Evaluation Metrics and Model Validation
Using ChatGPT for Prompt Engineering in ML
GitHub Copilot and Code Interpreter in Practice
AI-Enhanced Feature Engineering and Selection
AI-Assisted Model Tuning and Hyperparameter Optimization
Building an Explainable AI
Regression and Resource Efficiency
Clustering and Customer Segmentation
Responsible AI and Model Integrity
Becoming an AI-Empowered ML Practitioner
Future Trends and Best Practices of AI-Driven Scikit-learn
Index
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
Introduction to Machine Learning and AI-Assisted Coding
Getting Started with Prompt Engineering
Data Wrangling and Preprocessing
Classification and Regression
Clustering and Dimensionality
Evaluation Metrics and Model Validation
Using ChatGPT for Prompt Engineering in ML
GitHub Copilot and Code Interpreter in Practice
AI-Enhanced Feature Engineering and Selection
AI-Assisted Model Tuning and Hyperparameter Optimization
Building an Explainable AI
Regression and Resource Efficiency
Clustering and Customer Segmentation
Responsible AI and Model Integrity
Becoming an AI-Empowered ML Practitioner
Future Trends and Best Practices of AI-Driven Scikit-learn
Index
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