Название: Practical Machine Learning with R: Tutorials and Case Studies
Автор: Carsten Lange
Издательство: CRC Press
Год: 2024
Страниц: 369
Язык: английский
Формат: pdf (true)
Размер: 16.5 MB

This textbook is a comprehensive guide to Machine Learning and Artificial Intelligence tailored for students in business and economics. It takes a hands-on approach to teach Machine Learning, emphasizing practical applications over complex mathematical concepts. Students are not required to have advanced mathematics knowledge such as matrix algebra or calculus.

The author introduces Machine Learning algorithms, utilizing the widely used R language for statistical analysis. Each chapter includes examples, case studies, and interactive tutorials to enhance understanding. No prior programming knowledge is needed. The book leverages the tidymodels package, an extension of R, to streamline data processing and model workflows. This package simplifies commands, making the logic of algorithms more accessible by minimizing programming syntax hurdles. The use of tidymodels ensures a unified experience across various Machine Learning models.

This book introduces Machine Learning algorithms and explains the underlying concepts without using higher mathematics concepts like matrix algebra or cal- culus. Each chapter provides examples, case studies, and interactive tutorials. The examples and hands-on tutorials use the R language, which is widely used for statistical analysis and data science. R’s relatively simple syntax makes it easy for beginners to learn the language, making R a good choice for teaching Machine Learning. No prior programming skills are required to work with this book. A designated R chapter introduces the R skills needed for the course. In addition, each chapter offers one or more interactive R tutorials. Students can work with real-world data and use the interactive environment to learn and experiment with R code in a web browser.

With interactive tutorials that students can download and follow along at their own pace, the book provides a practical approach to apply Machine Learning algorithms to real-world scenarios.

In addition to the interactive tutorials, each chapter includes a Digital Resources section, offering links to articles, videos, data, and sample R code scripts.

This book is not just a textbook; it is a dynamic learning experience that empowers students and instructors alike with a practical and accessible approach to Machine Learning in business and economics.

Key Features:

Unlocks Machine Learning basics without advanced mathematics ― no calculus or matrix algebra required.
Demonstrates each concept with R code and real-world data for a deep understanding ― no prior programming knowledge is needed.
Bridges the gap between theory and real-world applications with hands-on interactive projects and tutorials in every chapter, guided with hints and solutions.
Encourages continuous learning with chapter-specific online resources―video tutorials, R-scripts, blog posts, and an online community.
Supports instructors through a companion website that includes customizable materials such as slides and syllabi to fit their specific course needs.