Practical Machine Learning in R

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John Wiley & Sons, May 27, 2020 - Computers - 464 pages

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.

 

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Contents

Discovering Knowledge in Data
5
Model Selection
14
Exercises
24
Chapterژ2 Introduction to
25
Writing and Running an R Script
41
Exercises
52
Chapterژ3 Managing Data
53
Data Exploration
60
Chapterژ7 Na´ve Bayes
251
Evaluating
264
51
266
Exercises
274
Chapterژ8 Decision Trees
277
52
300
Evaluating
305
Beyond Predictive Accuracy
321

Data Preparation
74
26
95
Exercises
100
Relationships Between Variables
106
Regression 101
137
38
148
Chapterژ5 Logistic Regression
165
Income Prediction
207
45
208
Exercises
216
Classification
221
Classification
223
47
233
208
237
Revisiting the Income
239
50
247
Exercises
249
Visualizing Model Performance
332
55
335
Chapterژ10 Improving Performance
341
Ensemble Methods
354
Exercises
366
Unsupervised
367
Discovering Association Rules
376
Identifying Grocery
386
Exercises
392
Chapterژ12 Grouping Data with
395
kMeans Clustering
399
Clustering the Data
416
Index
421
341
422
130
433
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About the author (2020)

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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