Machine learning data science de gutierrez daniel (29 resultados)
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Librería: Goodwill of Silicon Valley, SAN JOSE, CA, Estados Unidos de AmericaGoodwill of Silicon Valley
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Condición: good. Supports Goodwill of Silicon Valley job training programs. The cover and pages are in Good condition! Any other included accessories are also in Good condition showing use. Use can include some highlighting and writing, page and cover creases as well as other types visible wear.
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Condición: acceptable. Used - Acceptable: All pages and the cover are intact, but shrink wrap, dust covers, or boxed set case may be missing. Pages may include limited notes, highlighting, or minor water damage but the text is readable. Pages may include limited notes and highlighting, but the text cannot be obscured or unreadab…le. Any access codes or passwords originally included with the book may be expired, used or no longer valid. Image is stock photo and cover art edition may be different than pictured.
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Paperback. Condición: new. Paperback. A practitioner's tools have a direct impact on the success of his or her work. This book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised machine…learning, unsupervised machine learning and model evaluation. Machine learning and data science are large disciplines, requiring years of study in order to gain proficiency. This book can be viewed as a set of essential tools we need for a long-term career in the data science field recommendations are provided for further study in order to build advanced skills in tackling important data problem domains. The R statistical environment was chosen for use in this book. R is a growing phenomenon worldwide, with many data scientists using it exclusively for their project work. All of the code examples for the book are written in R. In addition, many popular R packages and data sets will be used. This book can be viewed as a set of essential tools we need for a long-term career in the data science field - recommendations are provided for further study in order to build advanced skills in tackling important data problem domains. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Librería: Rarewaves.com USA, London, LONDO, Reino UnidoRarewaves.com USA
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Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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Librería: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrlandaKennys Bookshop and Art Galleries Ltd.
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EUR 52,71
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Condición: New. Num Pages: 230 pages. BIC Classification: UNF. Category: (P) Professional & Vocational. Dimension: 235 x 190. . . 2015. First. Paperback. . . . .
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Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
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Librería: Chiron Media, Wallingford, , Reino UnidoChiron Media
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EUR 47,36
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Librería: Kennys Bookstore, Olney, MD, Estados Unidos de AmericaKennys Bookstore
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EUR 65,86
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Condición: New. Num Pages: 230 pages. BIC Classification: UNF. Category: (P) Professional & Vocational. Dimension: 235 x 190. . . 2015. First. Paperback. . . . . Books ship from the US and Ireland.
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Librería: Rarewaves USA United, OSWEGO, IL, Estados Unidos de AmericaRarewaves USA United
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Paperback. Condición: New.
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Librería: moluna, Greven, , Alemaniamoluna
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EUR 55,45
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Condición: New. KlappentextrnrnA practitioner s tools have a direct impact on the success of his or her work. This book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access.
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Librería: Gazelle Books, Lancaster, LANCA, Reino UnidoGazelle Books
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Librería: Rarewaves.com UK, London, Reino UnidoRarewaves.com UK
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Librería: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
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EUR 88,09
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Paperback. Condición: new. Paperback. A practitioner's tools have a direct impact on the success of his or her work. This book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised machine…learning, unsupervised machine learning and model evaluation. Machine learning and data science are large disciplines, requiring years of study in order to gain proficiency. This book can be viewed as a set of essential tools we need for a long-term career in the data science field recommendations are provided for further study in order to build advanced skills in tackling important data problem domains. The R statistical environment was chosen for use in this book. R is a growing phenomenon worldwide, with many data scientists using it exclusively for their project work. All of the code examples for the book are written in R. In addition, many popular R packages and data sets will be used. This book can be viewed as a set of essential tools we need for a long-term career in the data science field - recommendations are provided for further study in order to build advanced skills in tackling important data problem domains. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Librería: Rarewaves.com UK, London, Reino UnidoRarewaves.com UK
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EUR 49,29
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 68,68
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Taschenbuch. Condición: Neu. Neuware - A practitioner's tools have a direct impact on the success of his or her work. This book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised machine… learning, unsupervised machine learning and model evaluation. Machine learning and data science are large disciplines, requiring years of study in order to gain proficiency. This book can be viewed as a set of essential tools we need for a long-term career in the data science field - recommendations are provided for further study in order to build advanced skills in tackling important data problem domains.The R statistical environment was chosen for use in this book. R is a growing phenomenon worldwide, with many data scientists using it exclusively for their project work. All of the code examples for the book are written in R. In addition, many popular R packages and data sets will be used.
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Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de AmericaPBShop.store US
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EUR 44,46
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PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK
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PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Librería: THE SAINT BOOKSTORE, Southport, , Reino UnidoTHE SAINT BOOKSTORE
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EUR 47,39
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Paperback / softback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
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Paperback. Condición: new. Paperback. Build real-world machine learning solutions from scratch using R-no advanced math or prior coding experience required.This second edition of Machine Learning and Data Science offers an accessible, hands-on introduction to the core principles of machine learning, statistical modeling, and pra…ctical data science-without overwhelming readers with complex formulas or technical jargon. Perfect for beginners, analysts, and business professionals transitioning into data science, this book provides a complete project-based roadmap from data wrangling to model deployment using the powerful R programming language. Whether you're analyzing marketing trends, predicting customer behavior, or detecting fraud, this book equips you with the foundation needed to solve real problems using machine learning.Author and data scientist Daniel D. Gutierrez draws on his experience teaching at UCLA and years of industry practice to guide you through essential topics, including regression, classification, clustering, feature engineering, and model evaluation. You'll explore supervised and unsupervised learning techniques, apply visualization strategies, and build intuitive workflows that mirror the data science process used by professionals across finance, healthcare, marketing, and more. Unlike overly theoretical texts, this guide emphasizes application-what to do, why to do it, and how to do it in R.Inside, you'll find step-by-step tutorials, use case examples from Kaggle competitions, and easy-to-follow code snippets that let you apply machine learning concepts immediately. Learn how to access and clean real-world data sets, implement algorithms like decision trees, random forests, logistic regression, and k-means clustering, and avoid common pitfalls such as data leakage and overfitting. Move from exploratory data analysis to powerful predictive modeling.Whether you're a student, aspiring data scientist, or working analyst seeking to expand your skills, this is your essential, beginner-friendly guide to statistical learning and machine learning with R. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Librería: preigu, Osnabrück, Alemaniapreigu
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Taschenbuch. Condición: Neu. Machine Learning and Data Science, 2nd Edition | An Introduction to Statistical Learning Methods with R | Daniel Gutierrez | Taschenbuch | Englisch | 2025 | Technics Publications | EAN 9798898160067 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]…de | Anbieter: preigu Print on Demand.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 56,97
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Build real-world machine learning solutions from scratch using R-no advanced math or prior coding experience required.This second edition of Machine Learning and Data Science offers an accessible, hands-on introduction to the core princi…ples of machine learning, statistical modeling, and practical data science-without overwhelming readers with complex formulas or technical jargon. Perfect for beginners, analysts, and business professionals transitioning into data science, this book provides a complete project-based roadmap from data wrangling to model deployment using the powerful R programming language. Whether you're analyzing marketing trends, predicting customer behavior, or detecting fraud, this book equips you with the foundation needed to solve real problems using machine learning.Author and data scientist Daniel D. Gutierrez draws on his experience teaching at UCLA and years of industry practice to guide you through essential topics, including regression, classification, clustering, feature engineering, and model evaluation. You'll explore supervised and unsupervised learning techniques, apply visualization strategies, and build intuitive workflows that mirror the data science process used by professionals across finance, healthcare, marketing, and more. Unlike overly theoretical texts, this guide emphasizes application-what to do, why to do it, and how to do it in R.Inside, you'll find step-by-step tutorials, use case examples from Kaggle competitions, and easy-to-follow code snippets that let you apply machine learning concepts immediately. Learn how to access and clean real-world data sets, implement algorithms like decision trees, random forests, logistic regression, and k-means clustering, and avoid common pitfalls such as data leakage and overfitting. Move from exploratory data analysis to powerful predictive modeling.Whether you're a student, aspiring data scientist, or working analyst seeking to expand your skills, this is your essential, beginner-friendly guide to statistical learning and machine learning with R.
