John Wiley & Sons Practical Machine Learning in R Cover Guides professionals and students through the rapidly growing field of machine learning with hands-o.. Product #: 978-1-119-59151-1 Regular price: $35.42 $35.42 In Stock

Practical Machine Learning in R

Nwanganga, Fred / Chapple, Mike


1. Edition July 2020
464 Pages, Softcover
Wiley & Sons Ltd

ISBN: 978-1-119-59151-1
John Wiley & Sons

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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 the Authors vii

About the Technical Editors ix

Acknowledgments xi

Introduction xxi

Part I: Getting Started 1

Chapter 1 What is Machine Learning? 3

Chapter 2 Introduction to R and RStudio 25

Chapter 3 Managing Data 53

Part II: Regression 101

Chapter 4 Linear Regression 103

Chapter 5 Logistic Regression 165

Part III: Classification 221

Chapter 6 k-Nearest Neighbors 223

Chapter 7 Naïve Bayes 251

Chapter 8 Decision Trees 277

Part IV: Evaluating and Improving Performance 305

Chapter 9 Evaluating Performance 307

Chapter 10 Improving Performance 341

Part V: Unsupervised Learning 367

Chapter 11 Discovering Patterns with Association Rules 369

Chapter 12 Grouping Data with Clustering 395

Index 421