Machine Learning: Datasets for Classification with Python

In “What Is Machine Learning” (https://sci-en-tech.com/ebooks/), we introduced machine learning (ML) at a high level and summarized its core concepts. A central takeaway was that modern ML models are fundamentally data-driven. Consequently, building a reliable model depends critically on a well-prepared dataset; a thorough understanding, careful examination, and appropriate treatment of the data are essential for both effective training and rigorous testing.This booklet focuses specifically on some major publicly available datasets and their creation, examination, and treatment for training supervised ML models. Using several widely adopted benchmark datasets as case studies, we explore their origins, intended applications, and the reasons they have become standard references in ML research. We also provide practical demonstrations of how to load these datasets in Python—using scikit-learn or PyTorch—inspect their structures, and visualize representative data samples. These examples illustrate the fundamental principles of data management within a typical ML workflow.Our discussion centers on ML classification tasks, examining datasets that encompass both image recognition and structured data. In particular, we address the following aspects:• Data Normalization: Transforming features to a comparable scale to improve numerical stability and model convergence.• Data Splitting: Partitioning data into training, validation, and test sets to ensure fair and unbiased evaluation.• Data Statistics: Analyzing sample distributions and identifying class imbalances that may adversely affect model performance.These concepts are essential for ensuring that a model generalizes effectively to unseen data. Ultimately, this booklet provides an intuitive yet practical foundation for understanding how datasets are constructed and prepared for ML models. High-quality data is the cornerstone of high-quality machine learning: if the underlying data are flawed, the resulting model will inevitably reflect those flaws, regardless of the training algorithm employed. In short, no ML model can exceed the quality of its dataset.