Machine Learning for Knowledge Discovery with R: Methodologies for Modeling, Inference and Prediction

Machine Learning for Knowledge Discovery with R provides methodologies and examples for statistical modelling, inference, and prediction of data analysis, emphasizing statistical thinking, graphical methods, and real-world data examples. It is suitable for upper-level-undergraduate or graduate-level data analysis courses and as a useful desk-reference for data analysts.

Format: Hardback
Length: 244 pages
Publication date: 24 September 2021
Publisher: Taylor & Francis Ltd

Machine Learning for Knowledge Discovery with R is a comprehensive guide that provides methodologies and examples for statistical modelling, inference, and prediction of data analysis. It encompasses a wide range of recent supervised and unsupervised machine learning techniques, including recursive partitioning modelling, regularized regression, support vector machine, neural network, clustering, and causal-effect inference. Furthermore, the book emphasizes statistical thinking in data analysis, the use of statistical graphs for data structure exploration, and effective result presentations.

The book offers numerous real-world data examples from various domains such as life science and finance to illustrate the practical applications of the discussed methods. Key features of Machine Learning for Knowledge Discovery with R include:

Comprehensive Coverage: The book covers a wide range of supervised and unsupervised machine learning methodologies, providing a comprehensive theoretical foundation and practical applications.

Emphasis on Statistical Thinking: It emphasizes the importance of statistical thinking, judgment, graphical methods, and collaboration with subject-matter experts in data analysis, interpretation, and presentations.

Written by Practitioners: The book is written by a statistical data analysis practitioner, making it accessible and relevant to practitioners in scientific research or industrial applications.

Suitable for Data Analysis Courses: The book is suitable for upper-level-undergraduate or graduate-level data analysis courses, providing a solid foundation for understanding and applying machine learning techniques.

Useful Desk Reference: It serves as a valuable desk reference for data analysts, providing practical guidance and reference for data analysis tasks and methodologies.

In conclusion, Machine Learning for Knowledge Discovery with R is an essential resource for data analysts and practitioners seeking to leverage machine learning techniques for knowledge discovery and data analysis. With its comprehensive coverage, emphasis on statistical thinking, and practical applications, the book provides a valuable guide for advancing statistical modelling and inference in various fields.

Weight: 544g
Dimension: 161 x 243 x 22 (mm)
ISBN-13: 9781032065366