Master machine learning techniques with R to deliver insights in complex projects
About This BookUnderstand 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 guideWho This Book Is ForThis book is for data science professionals, data analysts, or anyone with a working knowledge of machine learning, with R who now want to take their skills to the next level and become an expert in the field.
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 AmazonIn DetailThis 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.
Style and approachThe book delivers practical and real-world solutions to problems and a variety of tasks such as complex recommendation systems. By the end of this book, you will have gained expertise in performing R machine learning and will be able to build complex machine learning projects using R and its packages.
