Introduction to Machine Learning with R: Rigorous Mathematical Analysis (1)

By: Scott V. Burger

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Synopsis: 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 trainingUnderstand machine learning algorithms for supervised and unsupervised casesExamine statistical concepts for designing data for use in modelsDive into linear regression models used in business and scienceUse single-layer and multilayer neural networks for calculating outcomesLook at how tree-based models work, including popular decision treesGet a comprehensive view of the machine learning ecosystem in RExplore the powerhouse of tools available in R’s caret package