Practical Machine Learning with R: Tutorials and Case Studies by Carsten Lange
Requirements: .PDF reader, 16.5 MB
Overview: 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. 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. 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.
Genre: Non-Fiction > Tech & Devices
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https://rapidgator.net/file/7869d939696 ... R.pdf.html
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Requirements: .PDF reader, 16.5 MB
Overview: 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. 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. 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.
Genre: Non-Fiction > Tech & Devices
Download Instructions:
https://tbit.to/tqm2k1y0pkan.html
https://katfile.com/gkbvtkdqpdwr/Practical_Machine_Learning_with_R.pdf.html
https://rapidgator.net/file/7869d939696 ... R.pdf.html
Trouble downloading? Read This.