Practical machine learning with R : tutorials and case studies / Carsten Lange.
"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 ha...
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| Online Access: |
Full Text (via Taylor & Francis) |
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| Main Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
[Place of publication not identified] :
Chapman and Hall/CRC,
2024.
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| Edition: | First edition. |
| Subjects: |
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| 100 | 1 | |a Lange, Carsten, |d 1960- |e author. |0 http://id.loc.gov/authorities/names/no98080024 |1 http://isni.org/isni/0000000070944999 | |
| 245 | 1 | 0 | |a Practical machine learning with R : |b tutorials and case studies / |c Carsten Lange. |
| 250 | |a First edition. | ||
| 264 | 1 | |a [Place of publication not identified] : |b Chapman and Hall/CRC, |c 2024. | |
| 300 | |a 1 online resource (xvi, 352 pages). | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a volume |b nc |2 rdacarrier | ||
| 545 | 0 | |a 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. | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a 1. Introduction2. Basics of Machine Learning3. Introduction to R and RStudio4. k-Nearest Neighbors -- Getting Started5. Linear Regression -- Key Machine Learning Concepts6. Polynomial Regression -- Overfitting & Tuning Explained7. Ridge, Lasso, and Elastic Net -- Regularization Explained8. Logistic Regression -- Handling Imbalanced Data9. Deep Learning -- MLP Neural Networks Explained10. Tree-Based Models -- Bootstrapping Explained11. Interpreting Machine Learning Results12. Concluding RemarksIndexBibliography | |
| 520 | |a "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. 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"-- |c Provided by publisher. | ||
| 588 | 0 | |a Online resource; title from PDF title page (Taylor & Francis, viewed May 16, 2024). | |
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