Key Features
Understand and apply machine learning methods using an extensive set of R packages such as XGBOOST
Understand the benefits and potential pitfalls of using machine learning methods such as Multi-Class Classification and Unsupervised Learning
Implement advanced concepts in machine learning with this example-rich guide
Book Description
This 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 learn
Gain deep insights into the application of machine learning tools in the industry
Manipulate data in R efficiently to prepare it for analysis
Master the skill of recognizing techniques for effective visualization of data
Understand why and how to create test and training data sets for analysis
Master fundamental learning methods such as linear and logistic regression
Comprehend advanced learning methods such as support vector