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AI-Assisted Research: From Data to Publication Without Coding: A Visual Guide for Engineering Researchers
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Paperback
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En stock
En stock
Peso con empaque:
0.55 kg
0.55 kg
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Sí
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Nuevo
Nuevo
Producto de:
Amazon
Amazon
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USA
USA
Sobre este producto
- AI-Assisted Research: From Data to Publication Without CodingA Visual Guide for Mechanical Engineering ResearchersArtificial Intelligence is transforming research—but most mechanical engineering researchers are held back by one barrier: coding.This book is written for PhD scholars, faculty members, post-doctoral researchers, and industry R&D engineers who want to use AI and machine learning in their research without spending months learning programming.AI-Assisted Research: From Data to Publication Without Coding is a visual, step-by-step guide that shows how modern AI tools can support the entire research workflow—from defining a research problem to producing journal-ready figures, tables, and results.Unlike theoretical AI textbooks, this book focuses on practical research execution using AI assistants such as ChatGPT and similar platforms. You will learn how to communicate effectively with AI, upload and analyze experimental data, build machine learning models, evaluate performance, and generate publication-quality outputs—all with minimal technical background.What You Will LearnHow AI is changing the way engineering research is conductedHow to choose the right AI assistant for your research needsHow to write effective prompts for data analysis and modelingHow to organize, clean, and validate experimental data using AIHow to perform exploratory data analysis with clear visualizationsHow to build regression, classification, and deep learning models without codingHow to evaluate and optimize model performanceHow to create professional figures, tables, and plots for journalsHow to responsibly use AI in research while maintaining scientific rigorReal Engineering Case StudiesThe book includes real, end-to-end examples from mechanical and materials engineering research, including:Composite material property predictionBattery thermal behavior analysisMachine learning-based regression and classificationImage-based defect detection and object recognitionEach case study follows a clear structure: problem → AI interaction → results → publication-ready output.Designed for ResearchersLarge-format pages (8.5" × 11") for clarityOver 60% visuals, including screenshots, flowcharts, and annotated figuresStep-by-step explanations with minimal jargonClear warnings about AI limitations, ethics, and reproducibilityWho This Book Is ForResearchers with experimental or simulation data but no coding skillsFaculty members integrating AI into research or supervisionPhD scholars seeking faster, more efficient research workflowsIndustry researchers who need results, not programming tutorialsWho This Book Is Not ForReaders looking for in-depth programming or algorithm derivationsComputer science–focused machine learning textbooks
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