Summary

Machine learning (ML) is a collection of programming techniques for discovering relationships in data. With ML algorithms, you can cluster and classify data for tasks like making recommendations or fraud detection and make predictions for sales trends, risk analysis, and other forecasts. Once the domain of academic data scientists, machine learning has become a mainstream business process, and tools like the easy-to-learn R programming language put high-quality data analysis in the hands of any programmer.

Machine Learning with R, the tidyverse, and mlr

teaches you widely used ML techniques and how to apply them to your own datasets using the R programming language and its powerful ecosystem of tools. This book will get you started!

About the book

Machine Learning with R, the tidyverse, and mlr

gets you started in machine learning using R Studio and the awesome mlr machine learning package. This practical guide simplifies theory and avoids needlessly complicated statistics or math. All core ML techniques are clearly explained through graphics and easy-to-grasp examples. In each engaging chapter, you’ll put a new algorithm into action to solve a quirky predictive analysis problem, including Titanic survival odds, spam email filtering, and poisoned wine investigation.

What's inside

Using the tidyverse packages to process and plot your data

Techniques for supervised and unsupervised learning

Classification, regression, dimension reduction, and clustering algorithms

Statistics primer to fill gaps in your knowledge

About the reader

For newcomers to machine learning with basic skills in R.

About the author

Hefin I. Rhys

is a senior laboratory research scientist at the Francis Crick Institute. He runs his own YouTube channel of screencast tutorials for R and RStudio.

Table of contents:

PART 1 - INTRODUCTION

1.Introduction to machine learning

2. Tidying, manipulating, and plotting data with the tidyverse

PART 2 - CLASSIFICATION

3. Classifying based on similarities with k-nearest neighbors

4. Classifying based on odds with logistic regression

5. Classifying by maximizing separation with discriminant analysis

6. Classifying with naive Bayes and support vector machines

7. Classifying with decision trees

8. Improving decision trees with random forests and boosting

PART 3 - REGRESSION

9. Linear regression

10. Nonlinear regression with generalized additive models

11. Preventing overfitting with ridge regression, LASSO, and elastic net

12. Regression with kNN, random forest, and XGBoost

PART 4 - DIMENSION REDUCTION

13. Maximizing variance with principal component analysis

14. Maximizing similarity with t-SNE and UMAP

15. Self-organizing maps and locally linear embedding

PART 5 - CLUSTERING

16. Clustering by finding centers with k-means

17. Hierarchical clustering

18. Clustering based on density: DBSCAN and OPTICS

19. Clustering based on distributions with mixture modeling

20. Final notes and further reading

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