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Machine Learning with Python
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Preface
Foundations
Machine-Learning Thinking and Problem Types
Data Preparation
Data Preparation for Machine Learning
Data Splitting and Leakage Prevention
Modeling
Baselines and First Models
Cross-Validation and Hyperparameter Tuning
Evaluation & Interpretation
Model Evaluation and Diagnostic Analysis
Model Interpretation and Explainability
Operational ML
Model Persistence and Reproducible Prediction
Batch Prediction and Operational Validation
Monitoring Drift and Model Performance
Practice & Governance
Responsible Machine Learning and Model Governance
End-to-End Machine Learning Case Study
References
Appendices
Appendix: Professional Machine-Learning Reference
Table of contents
Machine Learning with Python
A Structured Workflow for Predictive Modeling
Published
Sep 2026
Free Guide · Updated Sep 2026
Preface
