Author Description

Build machine learning algorithms, prepare data, and dig deep into data prediction techniques with RAbout This Book* Harness the power of R for statistical computing and data science* Explore, forecast, and classify data with R* Use R to apply common machine learning algorithms to real-world scenariosWho This Book Is ForPerhaps you already know a bit about machine learning but have never used R, or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.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 techniques in R to clean and prepare your data for analysis and visualize your results* Discover the different types of machine learning models and learn what is best to meet your data needs and solve data analysis problems* Classify your data with Bayesian and nearest neighbour methods* Predict values 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, and big dataIn DetailMachine 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's cross-platform, zero-cost statistical programming environment, there has never been a better time to start applying machine learning to your data. Machine learning with R offers a powerful set of methods to quickly and easily gain insight from your data to both, veterans and beginners in data analytics.Want to turn your data into actionable knowledge, predict outcome