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This book is for readers comfortable with basic Python who want a practical understanding of AI engineering. It develops the numerical foundations, explains the main modelling mechanisms, and follows their consequences into evaluation and deployment. Basic algebra is assumed; derivatives, probability, and matrix dimensions are introduced where needed.
Each numbered chapter includes an explanation, a standalone Python laboratory, an interpretation of its limits, and an extension exercise. Copy a complete code block into a Python file and run it in an environment with NumPy installed. The examples require no external dataset or paid model service. Small arrays keep the numerical operations visible and the runtime modest.
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