A 378-page book teaching practical approaches to building ML models by prioritizing data quality over algorithm complexity, covering data collection, cleaning, labeling, and synthetic data generation with Python. Published February 2024, it emphasizes responsible AI and the role of subject-matter experts in model development.

• Shifts focus from algorithm optimization to data quality as the foundation for robust, fair, and interpretable ML models
• Covers concrete techniques: data imputation, cleaning, labeling, augmentation, and synthetic data generation with scikit-learn code examples
• Introduces "small data" concept and strategies for handling missing data, addressing bias, and building ethical AI systems