Practical Machine Learning with R: Tutorials and Case Studies [Hardcover]

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.

Carsten Lange is an economics professor at Cal Poly Pomona with a keen interest in making data science and machine learning more accessible. He has authored multiple refereed articles and four books, including his 2004 book on applying neural networks for economics. Carsten is passionate about teaching machine learning and artificial intelligence with a focus on practical applications and hands-on learning.

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

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, tlӖ