What is a model? -- Supervised and unsupervised machine learning -- Sampling statistics and model training in R -- Regression in a nutshell -- Neural networks in a nutshell -- Tree-based methods -- Other advanced methods -- Machine learning with the caret package -- Encyclopedia of machine learning models in caret.
Summary:
Machine learning can be a difficult subject if you're not familiar with the basics. With this book, you'll get a solid foundation of introductory principles used in machine learning with the statistical programming language R. You'll start with the basics like regression, then move into more advanced topics like neural networks, and finally delve into the frontier of machine learning in the R world with packages like Caret. By developing a familiarity with topics like understanding the difference between regression and classification models, you'll be able to solve an array of machine learning problems. Knowing when to use a specific model or not can mean the difference between a highly accurate model and a completely useless one. This book provides copious examples to build a working knowledge of machine learning. Understand the major parts of machine learning algorithms Recognize how machine learning can be used to solve a problem in a simple manner Figure out when to use certain machine learning algorithms versus others Learn how to operationalize algorithms with cutting edge packages
This resource is supported by the Institute of Museum and Library Services under the provisions of the Library Services and Technology Act as administered by State Library of Iowa.