Machine learning data science par gutierrez daniel (29 résultats)
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Paperback. Etat : 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 lear…ning, 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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Etat : 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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Paperback. Etat : 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 lear…ning, 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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Taschenbuch. Etat : 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 lea…rning, 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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Paperback. Etat : 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 practic…al 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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Taschenbuch. Etat : 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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Taschenbuch. Etat : 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 principles… 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.
