4 Python ML books, from Pythonデータサイエンスハンドブック to 生成 Deep Learning

The starting point is a concrete failure: a first solo analysis project that scored well in cross-validation but dropped to roughly half its accuracy once real data replaced the test set. The author traces that to two causes — preprocessing leakage and a careless evaluation design — and draws a lesson about ordering rather than volume of knowledge: algorithm names without the fundamentals of raw-data handling and model validation do not help.

The four books are arranged to match that diagnosis. The first two build the foundation — NumPy and pandas internals, then feature engineering, evaluation and Pipeline practice — while the last two extend expressive range into deep learning and generative models. The author notes that the first book's machine learning section stays at the level of algorithm introductions, but that the earlier groundwork keeps reading speed up, and that the second book deliberately limits formulas in favor of decision-boundary diagrams and code.

The closing advice ties the list to specific symptoms rather than seniority: tangled data cleaning points to the handbook, validation-versus-production gaps to the scikit-learn book, unexamined deep learning use to the Keras volume, and curiosity about generative AI internals to the final title.