Key FeaturesBuild your confidence with R and find out how to solve a huge range of data-related problemsGet to grips with some of the most important machine learning techniques being used by data scientists and analysts across industries todayDont just learn – apply your knowledge by following featured practical projects covering everything from financial modeling to social media analysisBook DescriptionR is the established language of data analysts and statisticians around the world. And you shouldnt be afraid to use it...This Learning Path will take you through the fundamentals of R and demonstrate how to use the language to solve a diverse range of challenges through machine learning. Accessible yet comprehensive, it provides you with everything you need to become more a more fluent data professional, and more confident with R. In the first module youll get to grips with the fundamentals of R. This means youll be taking a look at some of the details of how the language works, before seeing how to put your knowledge into practice to build some simple machine learning projects that could prove useful for a range of real world problems.For the following two modules well begin to investigate machine learning algorithms in more detail. To build upon the basics, youll get to work on three different projects that will test your skills. Covering some of the most important algorithms and featuring some of the most popular R packages, theyre all focused on solving real problems in different areas, ranging from finance to social media.This Learning Path has been curated from three Packt products:R Machine Learning By Example By Raghav Bali, Dipanjan SarkarMachine Learning with R Learning - Second Edition By Brett LantzMastering Machine Learning with R By Cory LesmeisterWhat you will learnGet to grips with R techniques to clean and prepare your data for analysis, and visualize your resultsImplement R machine learning algorithms from scratch and be amazed to see the algorithms in actionSolve interesting real-world problems using machine learning and R as the journey unfoldsWrite reusable code and build complete machine learning systems from the ground upLearn specialized machine learning techniques for text mining, social network data, big data, and moreDiscover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problemsEvaluate and improve the performance of machine learning modelsLearn specialized machine learning techniques for text mining, social network data, big data, and moreAbout the AuthorRaghav Bali has a masters degree (gold medalist) in IT from the International Institute of Information Technology, Bangalore. He is an IT engineer at Intel, the worlds largest silicon company, where he works on analytics, business intelligence, and application development. He has worked as an analyst and developer in domains such as ERP, finance, and BI with some of the top companies in the world.Dipanjan Sarkar is an IT engineer at Intel, the worlds largest silicon company, where he works on analytics, business intelligence, and application development. He received his masters degree in information technology from the International Institute of Information Technology, Bangalore. His areas of specialization include software engineering, data science, machine learning, and text analytics. Dipanjans interests include learning about new technology, disruptive start-ups, and data science. Brett Lantz has spent more than 10 years using innovative data methods to understand human behavior. A trained sociologist, he was first enchanted by machine learning while studying a large database of teenagers social networking website profiles. Cory Lesmeister currently works as an advanced analytics consultant for Clarity Solution Group, where he applies the methods in this book to solve complex problems and provide actionable insights. Cory spent 16 years at Eli Lilly and Company in sales, market research, Lean Six Sigma, marketing analytics, and new product forecasting.Table of ContentsGetting Started with R and Machine LearningLets Help Machines LearnPredicting Customer Shopping Trends with Market Basket AnalysisBuilding a Product Recommendation SystemCredit Risk Detection and Prediction – Descriptive AnalyticsCredit Risk Detection and Prediction – Predictive AnalyticsSocial Media Analysis – Analyzing Twitter DataSentiment Analysis of Twitter DataIntroducing 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 TopicsA Process for SuccessLinear Regression – The Blocking and Tackling of Machine LearningLogistic Regression and Discriminant AnalysisAdvanced Feature Selection in Linear ModelsMore Classification Techniques – K-Nearest Neighbors and Support Vector MachinesClassification and Regression TreesNeural NetworksCluster AnalysisPrincipal Components AnalysisMarket Basket Analysis and Recommendation EnginesTime Series and CausalityText MiningR FundamentalsBibliography