Data Science, Analytics and Machine Learning with R

Автор: literator от 5-02-2023, 17:37, Коментариев: 0


Data Science, Analytics and Machine Learning with RНазвание: Data Science, Analytics and Machine Learning with R
Автор: Luiz Paulo Favero, Patrícia Belfiore, Rafael de Freitas Souza
Издательство: Academic Press/Elsevier
Год: 2023
Страниц: 662
Язык: английский
Формат: epub (true)
Размер: 155.1 MB

Data Science, Analytics and Machine Learning with R explains the principles of data mining and Machine Learning techniques and accentuates the importance of applied and multivariate modeling. The book emphasizes the fundamentals of each technique, with step-by-step codes and real-world examples with data from areas such as medicine and health, biology, engineering, technology and related sciences. Examples use the most recent R language syntax, with recognized robust, widespread and current packages. Code scripts are exhaustively commented, making it clear to readers what happens in each command. For data collection, readers are instructed how to build their own robots from the very beginning.

In addition, an entire chapter focuses on the concept of spatial analysis, allowing readers to build their own maps through geo-referenced data (such as in epidemiologic research) and some basic statistical techniques. Other chapters cover ensemble and uplift modeling and GLMM (Generalized Linear Mixed Models) estimations, both linear and nonlinear.

Presents a comprehensive and practical overview of Machine Learning, data mining and AI techniques for a broad multidisciplinary audience Serves readers who are interested in statistics, analytics and modeling, and those who wish to deepen their knowledge in programming through the use of R Teaches readers how to apply machine learning techniques to a wide range of data and subject areas Presents data in a graphically appealing way, promoting greater information transparency and interactive learning.

Table of Contents:
Part I: Introduction
Part II: Applied statistics and data visualization
Part III: Data mining and preparation
Part IV: Unsupervised machine learning techniques
Part V: Supervised machine learning techniques
Part VI: Improving performance
Part VII: Spatial analysis
Part VIII: Adding value to your work

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