Machine Learning with R: Expert techniques for predictive modeling

Front Cover
Packt Publishing Ltd, 2019 M04 15 - 458 pages

Solve real-world data problems with R and machine learning

Key Features
  • Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.6 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
Book Description

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 to R 3.6 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.

What you will 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
Who this book is for

Data scientists, students, and other practitioners who want a clear, accessible guide to machine learning with R.

 

Selected pages

Contents

Introducing Machine Learning
1
Managing and Understanding Data
29
Lazy Learning Classification Using Nearest Neighbors
65
Probabilistic Learning Classification Using Naive Bayes
89
Divide and Conquer Classification Using Decision Trees and Rules
125
Forecasting Numeric Data Regression Methods
167
Black Box Methods Neural Networks and Support Vector Machines
217
Finding Patterns Market Basket Analysis Using Association Rules
261
Finding Groups of Data Clustering with kmeans
287
Evaluating Model Performance
313
Improving Model Performance
347
Specialized Machine Learning Topics
375
Other Books You May Enjoy
423
Index
427
Copyright

Other editions - View all

Machine Learning with R - Third Edition
Brett Lantz
No preview available - 2019

Common terms and phrases

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About the author (2019)

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.

Bibliographic information

TitleMachine Learning with R: Expert techniques for predictive modeling
AuthorBrett Lantz
Edition3
PublisherPackt Publishing Ltd, 2019
ISBN1788291557, 9781788291552
Length458 pages
  
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