Machine Learning with R, the tidyverse, and mlr

Hefin Rhys

• Publisher: Manning
• Year: 2020
• Print ISBN: 9781617296574
• eBook ISBN: 9781638350170
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

SummaryMachine 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! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. 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 knowledgeAbout 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 - INTRODUCTION1.Introduction to machine learning2. Tidying, manipulating, and plotting data with the tidyversePART 2 - CLASSIFICATION3. Classifying based on similarities with k-nearest neighbors4. Classifying based on odds with logistic regression5. Classifying by maximizing separation with discriminant analysis6. Classifying with naive Bayes and support vector machines7. Classifying with decision trees8. Improving decision trees with random forests and boostingPART 3 - REGRESSION9. Linear regression10. Nonlinear regression with generalized additive models11. Preventing overfitting with ridge regression, LASSO, and elastic net12. Regression with kNN, random forest, and XGBoostPART 4 - DIMENSION REDUCTION13. Maximizing variance with principal component analysis14. Maximizing similarity with t-SNE and UMAP15. Self-organizing maps and locally linear embeddingPART 5 - CLUSTERING16. Clustering by finding centers with k-means17. Hierarchical clustering18. Clustering based on density: DBSCAN and OPTICS19. Clustering based on distributions with mixture modeling20. Final notes and further reading

Table of contents

• Copyright
• Brief Table of Contents
• Table of Contents
• Preface
• Acknowledgments
• About this book
• About the author
• About the cover illustration
• Part 1. Introduction
• Part 2. Classification
• Part 3. Regression
• Part 4. Dimension reduction
• Part 5. Clustering
• Appendix. Refresher on statistical concepts
• Index
• List of Figures
• List of Tables
• List of Listings