Lantz B. Machine Learning with R

Packt Publishing, 2013. — 396 p. — ISBN: 1782162143, 9781782162148Machine 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. Without shying away from the technical details, we will explore Machine Learning with R using clear and practical examples. Well-suited to machine learning beginners or those with experience. Explore R to find the answer to all of your questions.
How can we use machine learning to transform data into action? Using practical examples, we will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process.
We will learn how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data.
"Machine Learning with R" will provide you with the analytical tools you need to quickly gain insight from complex data.What you will learn from this bookUnderstand the basic terminology of machine learning and how to differentiate among various machine learning approaches
Use R to prepare data for machine learning
Explore and visualize data with R
Classify data using nearest neighbor methods
Learn about Bayesian methods for classifying data
Predict values using decision trees, rules, and support vector machines
Forecast numeric values using linear regression
Model data using neural networks
Find patterns in data using association rules for market basket analysis
Group data into clusters for segmentation
Evaluate and improve the performance of machine learning models
Learn specialized machine learning techniques for text mining, social network data, and big dataApproach
Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.