Übersicht			Auf mobile öffnen

Lieferstatus:	i.d.R. innert 7-14 Tagen versandfertig
Veröffentlichung:	August 2025
Genre:	EDV / Informatik
ISBN:	9798898160067
EAN-Code:	9798898160067
Verlag:	Technics Publications
Einband:	Kartoniert
Sprache:	English
Dimensionen:	H 235 mm / B 191 mm / D 21 mm
Gewicht:	744 gr
Seiten:	400
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

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.