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Machine Learning for Knowledge Discovery with R contains
methodologies and examples for statistical modelling, inference,
and prediction of data analysis. It includes many recent supervised
and unsupervised machine learning methodologies such as recursive
partitioning modelling, regularized regression, support vector
machine, neural network, clustering, and causal-effect inference.
Additionally, it emphasizes statistical thinking of data analysis,
use of statistical graphs for data structure exploration, and
result presentations. The book includes many real-world data
examples from life-science, finance, etc. to illustrate the
applications of the methods described therein. Key Features:
Contains statistical theory for the most recent supervised and
unsupervised machine learning methodologies. Emphasizes broad
statistical thinking, judgment, graphical methods, and
collaboration with subject-matter-experts in analysis,
interpretation, and presentations. Written by statistical data
analysis practitioner for practitioners. The book is suitable for
upper-level-undergraduate or graduate-level data analysis course.
It also serves as a useful desk-reference for data analysts in
scientific research or industrial applications.
Machine Learning for Knowledge Discovery with R contains
methodologies and examples for statistical modelling, inference,
and prediction of data analysis. It includes many recent supervised
and unsupervised machine learning methodologies such as recursive
partitioning modelling, regularized regression, support vector
machine, neural network, clustering, and causal-effect inference.
Additionally, it emphasizes statistical thinking of data analysis,
use of statistical graphs for data structure exploration, and
result presentations. The book includes many real-world data
examples from life-science, finance, etc. to illustrate the
applications of the methods described therein. Key Features:
Contains statistical theory for the most recent supervised and
unsupervised machine learning methodologies. Emphasizes broad
statistical thinking, judgment, graphical methods, and
collaboration with subject-matter-experts in analysis,
interpretation, and presentations. Written by statistical data
analysis practitioner for practitioners. The book is suitable for
upper-level-undergraduate or graduate-level data analysis course.
It also serves as a useful desk-reference for data analysts in
scientific research or industrial applications.
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