Are you looking to dive into the world of machine learning and harness the power of R? In today’s data-driven world, machine learning skills are increasingly valuable, and R has emerged as a leading language for statistical computing and machine learning. “Hands-On Machine Learning with R” is your guide to navigating this exciting field, offering practical modules designed to build your intuition and skills in applying machine learning techniques using R.
This comprehensive resource provides hands-on modules covering a wide array of essential machine learning methods, including:
- Generalized low rank models
- Clustering algorithms for data exploration
- Autoencoders for dimensionality reduction
- Regularized models for robust predictions
- Random forests for complex data patterns
- Gradient boosting machines for high accuracy
- Deep neural networks for advanced learning tasks
- Stacking and super learners for ensemble modeling
- And much more!
The focus is on practical application, teaching you how to build and fine-tune these models using R packages known for their scalability and reliability. While acknowledging the underlying mathematical principles, the book prioritizes developing your intuition for the strengths and weaknesses of each technique. Mathematical complexity is minimized where possible, with resources provided for those who wish to delve deeper into the theoretical details.
Who Will Benefit from This Guide?
This book is designed to be a practical companion for anyone eager to learn and apply machine learning. It serves as an accessible entry point for those new to machine learning and a valuable reference for experienced analysts looking to expand their R-based machine learning toolkit.
If you’re already comfortable with analytic methodologies, this book will guide you through implementing these techniques using various powerful R packages. While numerous online resources exist, this book addresses the common frustrations of inconsistency and incompleteness often found in online tutorials, offering a cohesive and comprehensive learning experience.
Please note: This book assumes a foundational understanding of R programming. Familiarity with basic R concepts such as defining functions, managing objects, and controlling program flow is expected. If you’re new to R, resources like “R for Data Science” by Wickham and Grolemund (2016) are excellent starting points to learn data manipulation, visualization, and exploration with R. For those seeking to deepen their R programming expertise, “Advanced R” by Wickham (2014) is highly recommended.
Furthermore, this book is not intended to be a deep theoretical exploration of machine learning algorithms. For in-depth mathematical foundations, consider resources such as “Elements of Statistical Learning” (Friedman, Hastie, and Tibshirani 2001), “Computer Age Statistical Inference” (Efron and Hastie 2016), and “Deep Learning” (Goodfellow, Bengio, and Courville 2016).
Instead, “Hands-On Machine Learning with R” focuses on empowering R users to effectively utilize the machine learning ecosystem within R. This includes mastering R packages like glmnet, h2o, ranger, xgboost, and lime to build insightful models from your data. The book champions a hands-on learning approach, fostering intuitive understanding through practical examples and just-enough theory. While reading alone is beneficial, actively experimenting with the provided code examples is strongly encouraged to solidify your learning.
Why Choose R for Machine Learning?
Over the past two decades, R has solidified its position as a premier tool for scientific computing and a leader in statistical methodologies for data analysis. Its strength in data science stems from its vibrant and expanding ecosystem of third-party packages. Key packages include:
- tidyverse: For streamlined data manipulation and analysis.
- h2o, ranger, xgboost: For high-performance and scalable machine learning algorithms.
- iml, pdp, vip: For enhancing machine learning model interpretability.
Numerous other valuable tools are introduced throughout the book, demonstrating R’s comprehensive capabilities in the machine learning domain.
Conventions to Guide Your Learning
To ensure a clear and effective learning experience, the book employs specific typographical conventions:
- strong italic: Indicates the introduction of new terms.
- bold: Denotes package and file names for easy identification.
inline code: Highlights functions and commands that you can directly type and execute in R.- Code chunks: Represent blocks of code intended for user execution.
<span>1</span> <span>+</span><span>2</span>
## [1] 3Throughout the book, you’ll also find visual cues within code chunks:
Tip or Suggestion: Highlights helpful tips and suggestions to enhance your learning.
Note: Indicates general notes and important information to consider.
Warning or Caution: Signals potential warnings or cautions to be aware of.
Expanding Your Knowledge with Additional Resources
The world of machine learning is vast, and continuous learning is key. This book serves as a strong foundation, and to further support your journey, it integrates numerous resources that we have found invaluable for deeper exploration and practical application. Due to print limitations, the physical book offers a condensed version of the available material. However, a wealth of online supplementary material awaits you at https://koalaverse.github.io/homlr/. This online resource is continuously updated with extended chapter content (e.g., random forest package benchmarking) and entirely new material (e.g., random hyperparameter search). Furthermore, you can access all the datasets used in the book, teaching resources like slides and exercises, and much more to enhance your learning experience.
Your Feedback is Valued
Reader feedback is highly appreciated as it helps improve future editions. To report any errors or bugs, please submit an issue at https://github.com/koalaverse/homlr/issues. Your contributions are essential to making this resource even better.
Acknowledgements to the Community
We extend our sincere gratitude to the numerous individuals who contributed feedback, typo corrections, and engaged in insightful discussions during the book’s development. The GitHub contributors, including (@)agailloty, (@)asimumba, (@)benprew, and many others listed in the original text, played a vital role in shaping this book. We also thank Alex Gutman, Greg Anderson, and the many colleagues who provided valuable input on the machine learning content.
Software and Environment Information
This book was meticulously crafted using the specified R packages and R version detailed in the original text. All code was executed on a 2017 MacBook Pro with the technical specifications listed, ensuring reproducibility and consistency. The comprehensive list of packages and session information is available in the original text for those interested in replicating the book’s computational environment.
