Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition (English Edition)

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

Introducing Machine Learning

Managing and Understanding Data

Lazy Learning – Classification Using Nearest Neighbors

Probabilistic Learning – Classification Using Naive Bayes

Divide and Conquer – Classification Using Decision Trees and Rules

Forecasting Numeric Data – Regression Methods

Black Box Methods – Neural Networks and Support Vector Machines

Finding Patterns – Market Basket Analysis Using Association Rules

Finding Groups of Data – Clustering with k-means

Evaluating Model Performance

Improving Model Performance

Specialized Machine Learning Topics

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