Machine Learning with R - Second Edition
Expert techniques for predictive modeling to solve all your data analysis problems
By: Brett Lantz
Publication Date: 2015-07-31
Number of pages: 452
Build machine learning algorithms, prepare data, and dig deep into data prediction techniques with RKey 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 Description
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'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 outcomes that make real impact, and have constantly developing insights? R gives you access to all the power you need to master exceptional machine learning techniques.
The second edition of Machine Learning with R provides you with an introduction to the essential skills required in 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 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 techniques in R to clean and prepare your data for analysis and visualize your resultsDiscover the different types of machine learning models and learn what is best to meet your data needs and solve data analysis problemsClassify your data with Bayesian and nearest neighbour methodsPredict values 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, and big dataTable 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 Performance