Master machine learning techniques with R to deliver insights in complex projects Key Features Understand and apply machine learning methods using an extensive set of R packages such as XGBOOSTUnderstand the benefits and potential pitfalls of using machine learning methods such as Multi-Class Classification and Unsupervised LearningImplement advanced concepts in machine learning with this example-rich guide Book DescriptionThis book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more.You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you'll understand how these concepts work and what they do.With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets. What you will learnGain deep insights into the application of machine learning tools in the industryManipulate data in R efficiently to prepare it for analysisMaster the skill of recognizing techniques for effective visualization of dataUnderstand why and how to create test and training data sets for analysisMaster fundamental learning methods such as linear and logistic regressionComprehend advanced learning methods such as support vector machinesLearn how to use R in a cloud service such as Amazon Это и многое другое вы найдете в книге Mastering Machine Learning with R - Second Edition. Advanced prediction, algorithms, and learning methods with R 3.x (Cory Lesmeister)