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!
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
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
