This is a book called "Data-Centric Machine Learning with Python" published by Packt on February 29, 2024. It's 378 pages and costs $37.79 for the eBook or $51.99 for paperback. The core argument flips the usual ML priority: instead of obsessing over algorithms, you should obsess over data quality. The book walks through practical work—data cleaning, labeling, augmentation, and synthetic data generation using Python. It also covers handling missing data through imputation techniques, semi-supervised learning, and addresses bias to build fair AI systems. The authors include Manmohan Gosada, a data science veteran who's led large-scale projects across industries. What sets this book apart from typical ML guides is the philosophical angle. Reviewers consistently mention the foreword and final chapter, which trace how data collection has worked historically and position data as the actual foundation of machine learning—not a supporting detail. This reframing matters because most people come to ML thinking the algorithms are what matter most, then discover their models fail because the data was garbage. The book introduces the concept of "small data"—the idea that you can accomplish real ML work with limited datasets if you handle them properly. The practical payoff is concrete: data preprocessing techniques with code snippets for scikit-learn, strategies for improving data collection and labeling, and methods for augmentation that actually work. One reader noted the book made them rethink their entire approach to data science, shifting from algorithm-tuning to data-quality focus. The eBook version includes an AI assistant (beta) to help with learning, and buying either the print or Kindle edition gives you a free PDF.