Machine Learning with R - Third Edition: Expert techniques for predictive modeling - Softcover

Lantz, Brett

Softcover

ISBN 10: 1788295862 ISBN 13: 9781788295864

Publisher: Packt Publishing, 2019

Synopsis

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.

Table of Contents

1. Introducing Machine Learning
2. Managing and Understanding Data
3. Lazy Learning – Classification Using Nearest Neighbors
4. Probabilistic Learning – Classification Using Naive Bayes
5. Divide and Conquer – Classification Using Decision Trees and Rules
6. Forecasting Numeric Data – Regression Methods
7. Black Box Methods – Neural Networks and Support Vector Machines
8. Finding Patterns – Market Basket Analysis Using Association Rules
9. Finding Groups of Data – Clustering with k-means
10. Evaluating Model Performance
11. Improving Model Performance
12. Specialized Machine Learning Topics

About this title

Review

"Brett has managed to hit the sweet spot: a book that is both readable by non-experts and at the same time so thorough that even experts can learn a lot!"

Prof. Dr. Holger K. von Jouanne-Diedrich, Author of the OneR package and Full Professor at TH Aschaffenburg University of Applied Sciences

About the Author

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.

Edition details

• Publisher: Packt Publishing
• Publication date: 2019
• ISBN 10: 1788295862
• ISBN 13: 9781788295864
• Binding: Paperback
• Language: English
• Edition number: 3
• Number of pages: 458