Machine Theory, Data Processing, Algorithms, Mathematical & Statistical 

Key FeaturesHarness the power of R for statistical computing and data scienceExplore, forecast, and classify data with RUse R to apply common machine learning algorithms to real-world scenariosBook DescriptionMachine 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 learnHarness the power of R to build common machine learning algorithms with real-world data science applicationsGet to grips with R techniques to clean and prepare your data for analysis, and visualize your resultsDiscover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problemsClassify your data with Bayesian and nearest neighbour methodsPredict values by using R to build decision trees, rules, and support vector machinesForecast numeric values with linear regression, and model your data with neural networksEvaluate and improve the performance of machine learning modelsLearn specialized machine learning techniques for text mining, social network data, big data, and moreAbout the AuthorBrett Lantz has used innovative data methods to understand human behavior for more than 10 years. A sociologist by training, he was first enchanted by machine learning while studying a large database of teenagers' social networking website profiles. Since then, he has worked on the interdisciplinary studies of cellular telephone calls, medical billing data, and philanthropic activity, among others.Table of ContentsIntroducing Machine LearningManaging and Understanding DataLazy Learning – Classification Using Nearest NeighborsProbabilistic Learning – Classification Using Naive BayesDivide and Conquer – Classification Using Decision Trees and RulesForecasting Numeric Data – Regression MethodsBlack Box Methods – Neural Networks and Support Vector MachinesFinding Patterns – Market Basket Analysis Using Association RulesFinding Groups of Data – Clustering with K-meansEvaluating Model PerformanceImproving Model PerformanceSpecialized Machine Learning Topics - from Amzon