Practical Machine Learning in R - Softcover

Nwanganga, Fred; Chapple, Mike

Softcover

ISBN 10: 1119591511 ISBN 13: 9781119591511

Publisher: Wiley, 2020

Synopsis

Guides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming language

Machine learning―a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructions―allows organizations to reveal patterns in their data and incorporate predictive analytics into their decision-making process. Practical Machine Learning in R provides a hands-on approach to solving business problems with intelligent, self-learning computer algorithms. 

Bestselling author and data analytics experts Fred Nwanganga and Mike Chapple explain what machine learning is, demonstrate its organizational benefits, and provide hands-on examples created in the R programming language. A perfect guide for professional self-taught learners or students in an introductory machine learning course, this reader-friendly book illustrates the numerous real-world business uses of machine learning approaches. Clear and detailed chapters cover data wrangling, R programming with the popular RStudio tool, classification and regression techniques, performance evaluation, and more. 

• Explores data management techniques, including data collection, exploration and dimensionality reduction
• Covers unsupervised learning, where readers identify and summarize patterns using approaches such as apriori, eclat and clustering
• Describes the principles behind the Nearest Neighbor, Decision Tree and Naive Bayes classification techniques
• Explains how to evaluate and choose the right model, as well as how to improve model performance using ensemble methods such as Random Forest and XGBoost

Practical Machine Learning in R is a must-have guide for business analysts, data scientists, and other professionals interested in leveraging the power of AI to solve business problems, as well as students and independent learners seeking to enter the field.

About this title

About the Author

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.

From the Back Cover

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

From the Inside Flap

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

Edition details

• Publisher: Wiley
• Publication date: 2020
• Language: English
• ISBN 10: 1119591511
• ISBN 13: 9781119591511
• Binding: Paperback
• Edition number: 1
• Number of pages: 464