Practical Machine Learning in R - Tapa blanda

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FRED NWANGANGA, PHD, is an assistant teaching professor of business analytics at the University of Notre Dame's Mendoza College of Business. He has over 15 years of technology leadership experience.

MIKE CHAPPLE, PHD, is associate teaching professor of information technology, analytics, and operations at the Mendoza College of Business. Mike is a bestselling author of over 25 books, and he currently serves as academic director of the University's Master of Science in Business Analytics program.

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INTRODUCING MACHINE LEARNING THROUGH THE INTUITIVE R PROGRAMMING LANGUAGE

Machine learning and data analytics have emerged as important avenues of value creation. Through machine learning, you can discover hidden patterns in data, leading to new ideas and understandings that might remain unknown without this powerful technique. Practical Machine Learning in R offers a hands-on introduction to working with large datasets using the R programming language, which is simple to understand and was built specifically for statistical analysis. Even if you have no prior coding experience, this book will show you how data scientists put machine learning into practice to generate business insights, solid predictions, and better decisions.

Unlike other books on the topic, Practical Machine Learning in R provides both a conceptual and technical introduction to machine learning. Examples and exercises use the R programming language and the latest data analytics tools, so you can get started without getting bogged down by advanced mathematics. With this book, machine learning techniques--from logistic regression to association rules and clustering--are within reach.

The only book to integrate an intuitive introduction to machine learning with step-by-step technical applications, Practical Machine Learning in R shows you how to:

• Conceptualize the different types of machine learning
• Discover patterns that exist within large datasets
• Begin writing and executing R scripts with RStudio
• Use R with Tidyverse to manage and visualize data
• Apply core statistical techniques like logistic regression and Naïve Bayes
• Evaluate and improve upon machine learning models