Machine Learning with R

Key Features

Book Description
Updated and upgraded to the latest libraries and most modern thinking, Machine Learning with R, Second Edition provides you with a rigorous introduction to this essential skill of professional data science. Without shying away from technical theory, it is written to provide focused and practical knowledge to get you building algorithms and crunching your data, with minimal previous experience.

With this book, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. Through full engagement with the sort of real-world problems data-wranglers face, you'll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering.
What you will learn
Harness the power of R to build common machine learning algorithms with realworld data science applicationsGet to grips with techniques in R to clean and prepare your data for analysis and visualize your resultsDiscover the different types of machine learning models and learn what is best to meet your data needs and solve data analysis problemsClassify your data with Bayesian and nearest neighbour methodsPredict values using R to build decision trees, rules, and support vector machinesForecast numeric values with linear regression and model your data with neural networksEvaluate and improve the performance of machine learning modelsLearn specialized machine learning techniques for text mining, social network data, and big data
Who this book is for
Perhaps you already know a bit about machine learning but have never used R, or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.