Machine Learning with R - Third Edition

More Information
Learn
  • Discover the origins of machine learning and how exactly a computer learns by example
  • Prepare your data for machine learning work with the R programming language
  • 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 — the basis of deep learning
  • Avoid bias in machine learning models
  • Evaluate your models and improve their performance
  • Connect R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlow
About

Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data.

Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings.

This new 3rd edition updates the classic R data science book with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.

Features
  • Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.5 and beyond
  • Harness the power of R to build flexible, effective, and transparent machine learning models
  • Learn quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner, Brett Lantz
Page Count 458
Course Length 13 hours 44 minutes
ISBN 9781788295864
Date Of Publication 14 Apr 2019
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

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.