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The Kaggle Book. Master data science competitions with machine learning, GenAI, and LLMs - Second Edition Luca Massaron, Bojan Tunguz, Konrad Banachewicz - Ediciones

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The Kaggle Book. Master data science competitions with machine learning, GenAI, and LLMs - Second Edition Luca Massaron, Bojan Tunguz, Konrad Banachewicz

Editorial: Packt Publishing (Z chęcią przeczytam książkę w języku polskim)

ISBN: 978-18-3508-863-0

ISBN sin guiones: 9781835088630

Tipo de cubierta: Softcover

Páginas: 706

Idioma: polaco (Polonia)

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The Kaggle Book. Master data science competitions with machine learning, GenAI, and LLMs - Second Edition Luca Massaron, Bojan Tunguz, Konrad Banachewicz

Luca Massaron

Kaggle has become the proving ground for millions of data enthusiasts worldwide, offering what no classroom tutorial can match: battle-tested skills built through real-world challenges and the hands-on experience that employers seek. Every competition sharpens your data analysis skills, expands your network within the data scientist community, and gives compelling proof of expertise to unlock career opportunities. The first book of its kind, The Kaggle Book brings together everything you need to excel in competitions, data science projects, and beyond. This new edition includes fresh content and new chapters on Kaggle Models, time series, and Generative AI competitions, with three Kaggle Grandmasters guiding you through modeling strategies and sharing hard-earned insights accumulated over years of competition. The book extends far past competition tactics, revealing techniques for tackling image, tabular, and textual data as well as reinforcement learning tasks. You’ll also discover tips for designing better validation schemes and working confidently with both standard and unconventional evaluation metrics. Whether you want to climb the Kaggle leaderboard, accelerate your data science career, or improve the accuracy of your models, this book is for you. Join our Discord community of over 1,000 members to learn, share, and grow together!