Machine learning with R

✍ Scribed by Brett Lantz

• Publisher: Packt Publishing
• Year: 2013
• Tongue: English
• Leaves: 396
• Series: Community experience distilled
• Edition: 1
• Category: Library

✦ Synopsis

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. 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
• Understand 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” data