Machine Learning with R - Second Edition

More Information
Learn
  • Harness the power of R to build common machine learning algorithms with real-world data science applications
  • Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results
  • Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems
  • Classify your data with Bayesian and nearest neighbor methods
  • Predict values by using R to build decision trees, rules, and support vector machines
  • Forecast numeric values with linear regression, and model your data with neural networks
  • Evaluate and improve the performance of machine learning models
  • Learn specialized machine learning techniques for text mining, social network data, big data, and more
About

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.

Features
  • Harness the power of R for statistical computing and data science
  • Explore, forecast, and classify data with R
  • Use R to apply common machine learning algorithms to real-world scenarios
Page Count 452
Course Length 13 hours 33 minutes
ISBN 9781784393908
Date Of Publication 30 Jul 2015
Understanding nearest neighbor classification
Example – diagnosing breast cancer with the k-NN algorithm
Summary
Understanding Naive Bayes
Example – filtering mobile phone spam with the Naive Bayes algorithm
Summary
Understanding decision trees
Example – identifying risky bank loans using C5.0 decision trees
Understanding classification rules
Example – identifying poisonous mushrooms with rule learners
Summary
Understanding regression
Example – predicting medical expenses using linear regression
Understanding regression trees and model trees
Example – estimating the quality of wines with regression trees and model trees
Summary
Understanding neural networks
Example – Modeling the strength of concrete with ANNs
Understanding Support Vector Machines
Example – performing OCR with SVMs
Summary
Understanding association rules
Example – identifying frequently purchased groceries with association rules
Summary
Understanding clustering
Example – finding teen market segments using k-means clustering
Summary

Authors

Brett Lantz

Brett Lantz (@DataSpelunking) has spent more than 10 years using innovative data methods to understand human behavior. A sociologist by training, Brett was first captivated by machine learning during research on a large database of teenagers' social network profiles. Brett is a DataCamp instructor and a frequent speaker at machine learning conferences and workshops around the world. He is known to geek out about data science applications for sports, autonomous vehicles, foreign language learning, and fashion, among many other subjects, and hopes to one day blog about these subjects at Data Spelunking, a website dedicated to sharing knowledge about the search for insight in data.