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With the rapidly advancing fields of Data Analytics and
Computational Statistics, it's important to keep up with current
trends, methodologies, and applications. This book investigates the
role of data mining in computational statistics for machine
learning. It offers applications that can be used in various
domains and examines the role of transformation functions in
optimizing problem statements. Data Analytics, Computational
Statistics, and Operations Research for Engineers: Methodologies
and Applications presents applications of computationally intensive
methods, inference techniques, and survival analysis models. It
discusses how data mining extracts information and how machine
learning improves the computational model based on the new
information. Those interested in this reference work will include
students, professionals, and researchers working in the areas of
data mining, computational statistics, operations research, and
machine learning.
This book is a comprehensive, hands-on guide to the basics of data
mining and machine learning with a special emphasis on supervised
and unsupervised learning methods. The book lays stress on the new
ways of thinking needed to master in machine learning based on the
Python, R, and Java programming platforms. This book first provides
an understanding of data mining, machine learning and their
applications, giving special attention to classification and
clustering techniques. The authors offer a discussion on data
mining and machine learning techniques with case studies and
examples. The book also describes the hands-on coding examples of
some well-known supervised and unsupervised learning techniques
using three different and popular coding platforms: R, Python, and
Java. This book explains some of the most popular classification
techniques (K-NN, Naïve Bayes, Decision tree, Random forest,
Support vector machine etc,) along with the basic description of
artificial neural network and deep neural network. The book is
useful for professionals, students studying data mining and machine
learning, and researchers in supervised and unsupervised learning
techniques.
Blockchain Technology for Emerging Applications: A Comprehensive
Approach explores recent theories and applications of the execution
of blockchain technology. Chapters look at a wide range of
application areas, including healthcare, digital physical
frameworks, web of-things, smart transportation frameworks,
interruption identification frameworks, ballot-casting,
architecture, smart urban communities, and digital rights
administration. The book addresses the engineering, plan
objectives, difficulties, constraints, and potential answers for
blockchain-based frameworks. It also looks at blockchain-based
design perspectives of these intelligent architectures for
evaluating and interpreting real-world trends. Chapters expand on
different models which have shown considerable success in dealing
with an extensive range of applications, including their ability to
extract complex hidden features and learn efficient representation
in unsupervised environments for blockchain security pattern
analysis.
This book is a comprehensive, hands-on guide to the basics of data
mining and machine learning with a special emphasis on supervised
and unsupervised learning methods. The book lays stress on the new
ways of thinking needed to master in machine learning based on the
Python, R, and Java programming platforms. This book first provides
an understanding of data mining, machine learning and their
applications, giving special attention to classification and
clustering techniques. The authors offer a discussion on data
mining and machine learning techniques with case studies and
examples. The book also describes the hands-on coding examples of
some well-known supervised and unsupervised learning techniques
using three different and popular coding platforms: R, Python, and
Java. This book explains some of the most popular classification
techniques (K-NN, Naive Bayes, Decision tree, Random forest,
Support vector machine etc,) along with the basic description of
artificial neural network and deep neural network. The book is
useful for professionals, students studying data mining and machine
learning, and researchers in supervised and unsupervised learning
techniques.
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