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Machine Learning with R

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  • 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

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.

  • Harness the power of R for statistical computing and data science
  • Use R to apply common machine learning algorithms with real-world applications
  • Prepare, examine, and visualize data for analysis
  • Understand how to choose between machine learning models
  • Packed with clear instructions to explore, forecast, and classify data
Page Count 396
Course Length 11 hours 52 minutes
ISBN 9781782162148
Date Of Publication 24 Oct 2013
Understanding naive Bayes
Example – filtering mobile phone spam with the naive Bayes algorithm
Understanding decision trees
Example – identifying risky bank loans using C5.0 decision trees
Understanding classification rules
Example – identifying poisonous mushrooms with rule learners
Understanding regression
Example – predicting medical expenses using linear regression
Understanding regression trees and model trees
Example – estimating the quality of wines with regression trees and model trees
Understanding association rules
Example – identifying frequently purchased groceries with association rules


Brett Lantz

Brett Lantz (@DataSpelunking) has spent more than 10 years using innovative data methods to understand human behavior. A sociologist by training, Brett was first captivated by machine learning during research on a large database of teenagers' social network profiles. Brett is a DataCamp instructor and a frequent speaker at machine learning conferences and workshops around the world. He is known to geek out about data science applications for sports, autonomous vehicles, foreign language learning, and fashion, among many other subjects, and hopes to one day blog about these subjects at Data Spelunking, a website dedicated to sharing knowledge about the search for insight in data.