Machine Learning with R, Second Edition

Machine learning, at its core, is concerned with transforming data into actionable knowledge. This makes machine learning well suited to the present-day era of big data. 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 to your data. Whether you are new to data analytics or a veteran, machine learning with R offers a powerful set of methods to quickly and easily gain insights from your data.
Want to turn your data into actionable knowledge, predict outcomes that make real impact, and have constantly developing insights? R gives you access to the cutting-edge power you need to master exceptional machine learning techniques.
Updated and upgraded to the latest libraries and most modern thinking, the second edition of Machine Learning with R provides you with a rigorous introduction to this essential skill of professional data science. Without shying away from technical theory, it is written to provide focused and practical knowledge to get you building algorithms and crunching your data, with minimal previous experience.
With this book you’ll discover all the analytical tools you need to gain insights from complex data and learn how to to choose the correct algorithm for your specific needs. Through full engagement with the sort of real-world problems data-wranglers face, you’ll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering. Transform the way you think about data; discover machine learning with R.
What you will learn
Harness the power of R to build common machine learning algorithms with real-world data science applications
Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results
Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems
Classify your data with Bayesian and nearest neighbour methods
Predict values by using R to build decision trees, rules, and support vector machines
Forecast numeric values with linear regression, and model your data with neural networks
Evaluate and improve the performance of machine learning models
Learn specialized machine learning techniques for text mining, social network data, big data, and more