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.
Inhaltsverzeichnis
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
