1
Frontmatter
5 chapters2
Part I: The ML Mindset
5 chapters3
Part II: Feature Engineering and Data Preparation
7 chapters- Part II: Feature Engineering and Data Preparation
- Chapter 5: SQL for Data Scientists — Window Functions, CTEs, and Query Optimization
- Chapter 6: Feature Engineering
- Chapter 7: Handling Categorical Data
- Chapter 8: Missing Data Strategies
- Chapter 9: Feature Selection
- Chapter 10: Building Reproducible Data Pipelines
4
Part III: Supervised Learning
10 chapters- Part III: Supervised Learning
- Chapter 11: Linear Models Revisited
- Chapter 12: Support Vector Machines
- Chapter 13: Tree-Based Methods
- Chapter 14: Gradient Boosting
- Chapter 15: Naive Bayes and Nearest Neighbors
- Chapter 16: Model Evaluation Deep Dive
- Chapter 17: Class Imbalance and Cost-Sensitive Learning
- Chapter 18: Hyperparameter Tuning
- Chapter 19: Model Interpretation
5
Part IV: Unsupervised Learning
6 chapters6
Part V: Specialized Data Types
5 chapters7
Part VI: From Notebook to Production
7 chapters8
Part VII: Synthesis
3 chapters9
Appendices
13 chapters- Glossary
- Answers to Selected Exercises and Quiz Questions
- Bibliography
- Appendix A: Python ML Library Quick Reference
- Appendix B: SQL Quick Reference for Data Scientists
- Appendix C: Math Reference for Machine Learning
- Appendix D: Environment Setup Guide
- Appendix E: Dataset Catalog
- Appendix F: Evaluation Metrics Reference
- Appendix G: Templates and Checklists
- Appendix H: Frequently Asked Questions
- Appendix I: Key Papers and Resources
- Index
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