Machine Learning with R: Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data, Edition 4

• Get to grips with the tidyverse, challenging data, and big data
• Create clear and concise data and model visualizations that effectively communicate results to stakeholders
• Solve a variety of problems using regression, ensemble methods, clustering, deep learning, probabilistic models, and more

• Learn the end-to-end process of machine learning from raw data to implementation
• Classify important outcomes using nearest neighbor and Bayesian methods
• Predict future events using decision trees, rules, and support vector machines
• Forecast numeric data and estimate financial values using regression methods
• Model complex processes with artificial neural networks
• Prepare, transform, and clean data using the tidyverse
• Evaluate your models and improve their performance
• Connect R to SQL databases and emerging big data technologies such as Spark, Hadoop, H2O, and TensorFlow

This book is designed to help data scientists, actuaries, data analysts, financial analysts, social scientists, business and machine learning students, and any other practitioners who want a clear, accessible guide to machine learning with R. No R experience is required, although prior exposure to statistics and programming is helpful.