Practical Machine Learning in R
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.
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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Discovering Knowledge in Data
Chapterژ2 Introduction to
Writing and Running an R Script
Chapterژ3 Managing Data
Chapterژ7 Na´ve Bayes
Chapterژ8 Decision Trees
Beyond Predictive Accuracy
Relationships Between Variables
Chapterژ5 Logistic Regression
Revisiting the Income
Visualizing Model Performance
Chapterژ10 Improving Performance
Discovering Association Rules
Chapterژ12 Grouping Data with
Clustering the Data