Machine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles. Using the R programming language, you’ll first start to learn with regression modelling and then move into more advanced topics such as neural networks and tree-based methods.

Finally, you’ll delve into the frontier of machine learning, using the caret package in R. Once you develop a familiarity with topics such as the difference between regression and classification models, you’ll be able to solve an array of machine learning problems. Author Scott V. Burger provides several examples to help you build a working knowledge of machine learning.

Explore machine learning models, algorithms, and data training
Understand machine learning algorithms for supervised and unsupervised cases
Examine statistical concepts for designing data for use in models
Dive into linear regression models used in business and science
Use single-layer and multilayer neural networks for calculating outcomes
Look at how tree-based models work, including popular decision trees
Get a comprehensive view of the machine learning ecosystem in R
Explore the powerhouse of tools available in R’s caret package

About the Author

Scott Burger is a senior data scientist living and working in Seattle. His programming experience comes from the realm of astrophysics, but he uses it in many different types of scenarios ranging from business intelligence to database optimizations. Scott has built a solid career on explaining terse scientific concepts to the general public and wants to use that expertise to shed light on the world of machine learning for the general R user.

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