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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.
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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