Machine Learning with R, the tidyverse, and mlr
- Paperback: 536 pages
- Publisher: WOW! eBook; 1st edition (March 31, 2020)
- Language: English
- ISBN-10: 1617296570
- ISBN-13: 978-1617296574
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 techniques accurately and efficiently identify patterns and relationships in data and use those models to make predictions about new data. ML techniques can work on even relatively small datasets, making these skills a powerful ally for nearly any data analysis task. The R programming language was designed with mathematical and statistical applications in mind. Small datasets are its sweet spot, and its modern data science tools, including the popular tidyverse package, make R a natural choice for ML.
Machine Learning with R, the tidyverse, and mlr teaches you how to gain valuable insights from your data using the powerful R programming language. In his engaging and informal style, author and R expert Hefin Ioan Rhys lays a firm foundation of ML basics and introduces you to the tidyverse, a powerful set of R tools designed specifically for practical data science. Armed with the fundamentals, you’ll delve deeper into commonly used machine learning techniques including classification, prediction, reduction, and clustering algorithms, applying each to real data to make predictions on fun and interesting problems.
- Commonly used ML techniques
- Using the tidyverse packages to organize and plot your data
- Validating model performance
- Choosing the best ML model for your task
- A variety of hands-on coding exercises
- ML best practices
Using the tidyverse packages, you’ll transform, clean, and plot your data, onboarding data science best practices as you go. To simplify your learning process, you’ll also use R’s mlr package, an incredibly flexible interface for a variety of core algorithms that allows you to perform complicated ML tasks with minimal coding. You’ll explore essential concepts like overfitting, underfitting, validating model performance, and how to choose the best model for your task. Illuminating visuals provide clear explanations, cementing your new knowledge.
Whether you’re tackling business problems, crunching research data, or just a data-minded developer, you’ll be building your own ML pipelines in no time with this Machine Learning with R, the tidyverse, and mlr hands-on tutorial!