Machine learning with R : learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications / Brett Lantz.

Includes index.

Chapter 1: Introducing machine learning--
Chapter 2: Managing and understanding data--
Chapter 3: Lazy learning-classification using nearest neighbors--
Chapter 4: Probabilistic learning-classification using naive Bayes--
Chapter 5: Divide and conquer-classification using decision trees and rules--
Chapter 6: Forecasting numeric data-regression methods--
Chapter 7: Black box methods-neural networks and support vector machines--
Chapter 8: Finding patterns-market basket analysis using association rules--
Chapter 9: Finding groups of data-clustering with k-means--
Chapter 10: Evaluating model performance--
Chapter 11: Improving model performance--
Chapter 12: Specialized machine learning topics--
Summary--
Index.

Machine Learning With R: Learn How to Use R to Apply Powerful Machine Learning Methods and Gain an Insight into Real-World Applications Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of "big data" and "data science". Given the growing prominence of R-a cross-platform, zero-cost statistical programming environment-there has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from your data. "Machine Learning with R" is a practical tutorial that uses hands-on examples to step through real-world application of machine learning.