If science fiction stories are to be believed, the invention of Artificial Intelligence inevitably leads to apocalyptic wars between machines and their makers. Thankfully, at the time of this writing, machines still require user input.
Though your impressions of Machine Learning may be colored by these mass-media depictions, today’s algorithms are too application-specific to pose any danger of becoming self-aware. The goal of today’s Machine Learning is not to create an artificial brain, but rather to assist us with making sense of the world’s massive data stores.
Conceptually, the learning process involves the abstraction of data into a structured representation, and the generalization of the structure into action that can be evaluated for utility. In practical terms, a machine learner uses data containing examples and features of the concept to be learned, then summarizes this data in the form of a model, which is used for predictive or descriptive purposes.
The field of machine learning provides a set of algorithms that transform data into actionable knowledge. Among the many possible methods, machine learning algorithms are chosen on the basis of the input data and the learning task. This fact makes machine learning well-suited to the present-day era of big data. Machine Learning with R, Third Edition introduces you to the fundamental concepts that define and differentiate the most commonly used machine learning approaches and how easy it is to use R to start applying machine learning to real-world problems.
Many of the algorithms needed for machine learning are not included as part of the base R installation. Instead, the algorithms are available via a large community of experts who have shared their work freely. These powerful tools are available to download at no cost, but must be installed on top of base R manually. This book covers a small portion of all of R’s machine learning packages and will get you up to speed with the learning landscape of machine learning with R.
Machine Learning with R, Third Edition updates the classic R data science book with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings.