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Full Description
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.
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. A companion website further enriches the learning and teaching experience: https://ai.lange-analytics.com.
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.
Contents
1. Introduction 2. Basics of Machine Learning 3. Introduction to R and RStudio 4. k-Nearest Neighbors — Getting Started 5. Linear Regression — Key Machine Learning Concepts 6. Polynomial Regression — Overfitting & Tuning Explained 7. Ridge, Lasso, and Elastic Net — Regularization Explained 8. Logistic Regression — Handling Imbalanced Data 9. Deep Learning — MLP Neural Networks Explained 10. Tree-Based Models — Bootstrapping Explained 11. Interpreting Machine Learning Results 12. Concluding Remarks Index Bibliography
