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Learn NumPy & SciPy for Machine Learning and Scientific Data Analysis: A Hands-On, Project-Based Guide to Scientific Python, Data Preparation, Linear Algebra, Statistics, and Optimization
Format:
Paperback
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
0.50 kg
0.50 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Stop shipping “plausible” numbers. NumPy and SciPy are the computational backbone of modern machine learning and scientific data analysis and they reward developers who can think in arrays, shapes, dtypes, and stable numerical methods. This book is built around the real skills that separate code that runs from results you can defend: vectorization, broadcasting, trustworthy algorithms, and reproducible scientific Python workflows.This is not a bloated reference manual or a theory-heavy ML textbook. It’s a hands-on, project-based training path designed around professional habits: sanity checks, shape/dtype inspection, data leakage prevention, stable comparisons, performance profiling, and lightweight testing so your work stays correct weeks late not just in one notebook session. It aligns with modern, practical NumPy/SciPy usage where reliability and reproducibility are non-negotiable.You’ll learn how to open a new dataset and immediately know what to check first, how to validate assumptions, how to transform data without corrupting it, and how to choose SciPy methods you can trust when the problem stops looking like a tutorial. By the end, you won’t just “know NumPy and SciPy” you’ll be able to build ML-ready pipelines, fit models via optimization, quantify uncertainty, and explain outputs with confidence.WHAT’S INSIDE Shape + dtype mastery that prevents silent bugs before they spread Vectorization & broadcasting mental models that turn slow loop code into clean, fast array logic Data preparation for machine learning: missing values, outliers, scaling, and data leakage defenses Linear algebra you actually use: norms, distances, least squares, decompositions and a regression solver from scratch Statistics that supports real decisions: bootstrapping, confidence intervals, practical hypothesis testing SciPy “algorithm layer” essentials: interpolation, integration, differential equations, and scientific case studies Optimization you can diagnose: objective design, method choice, convergence failures, and model calibration Reliability + performance engineering: numerical stability, profiling, memory (views vs copies), and reusable quality checklists Capstone projects that prove the skill: ML-ready analysis pipeline, optimization-driven fitting, and time-series/scientific workflowIf you already know basic Python but still feel shaky when shapes get complicated, results look “reasonable” but suspicious, performance slows down, or your notebook won’t reproduce cleanly this was written for you. It’s ideal for data scientists, ML engineers, analysts, researchers, and developers who want scientific Python skills that hold up under real-world pressure.Each chapter is built like training: a small set of skills → exercises → a checkpoint that becomes part of your reusable toolkit. That structure means you progress fast without cutting corners building real NumPy + SciPy competence in weeks, not months, because every chapter produces something you can reuse in your next project (inspectors, sanity checks, preprocessing pipelines, stats toolkit, quality checklist).If you’re ready to stop guessing and start working like someone who can prove their analysis is correct, stable, and reproducible, grab Learn NumPy & SciPy for Machine Learning and Scientific Data Analysis now. Open your next dataset with control, build ML-ready pipelines you can trust, and turn “it runs” into “it’s right.”
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U$S 36,08
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