Machine learning techniques can accurately and efficiently identify relationships and patterns in data. With the insights and predictive power these discoveries provide, ML is revolutionizing business, finance, the medical field, disaster prediction, and even the arts. With the easy-to-learn programming language R and its powerful ecosystem of tools, any programmer can achieve high-quality data analysis results.
about the book
Exploring Machine learning with R and mlr
features three chapters from Machine learning with R, tidyverse, and mlr
by author and veteran research scientist Hefin I. Rhys. In the first chapter, you’ll get familiar with common machine learning terminology and different types of machine learning. Next, you’ll gain a solid foundation in the mlr package, R's machine learning answer to Python's scikit-learn. You’ll also drill down into more advanced machine learning theory while learning your first algorithm: k-nearest neighbors. In the final chapter, you’ll explore some of the most commonly used ML techniques including decision trees and ensembling, which can drastically improve the performance of an algorithm. This short but substantial guide is a great way to jumpstart your machine learning education.
From Machine learning with R, tidyverse, and mlr
by Hefin I. Rhys:
- Chapter 1 - “Introduction”
- Chapter 3 - “Classifying based on similar observations: the k-Nearest neighbors algorithm”
- Chapter 7 - “Classifying with trees: Decision trees, random forests and gradient boosting”
about the author
Hefin Ioan Rhys
is a senior laboratory research scientist in the Flow Cytometry Shared Technology Platform at The Francis Crick Institute. He spent the final year of his PhD program teaching basic R skills at the university. A data science and machine learning enthusiast, he has his own Youtube channel featuring screencast tutorials in R and R Studio.