Sanjay Chakraborty – författare

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, 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, 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 covers various cutting-edge computing technologies and their applications over data. It discusses in-depth knowledge on big data and cloud computing, quantum computing, cognitive computing, and computational biology with respect to different kinds of data analysis and applications. In this book, authors describe some interesting models in the cloud, quantum, cognitive, and computational biology domains that provide some useful impact on intelligent data (emotional, image, etc.) analysis. They also explain how these computing technologies based data analysis approaches used for various real-life applications. The book will be beneficial for readers working in this area.

This book covers various cutting-edge computing technologies and their applications over data. It discusses in-depth knowledge on big data and cloud computing, quantum computing, cognitive computing, and computational biology with respect to different kinds of data analysis and applications. In this book, authors describe some interesting models in the cloud, quantum, cognitive, and computational biology domains that provide some useful impact on intelligent data (emotional, image, etc.) analysis. They also explain how these computing technologies based data analysis approaches used for various real-life applications. The book will be beneficial for readers working in this area.

This book covers various cutting-edge computing technologies and their applications over data. It discusses in-depth knowledge on big data and cloud computing, quantum computing, cognitive computing, and computational biology with respect to different kinds of data analysis and applications. In this book, authors describe some interesting models in the cloud, quantum, cognitive, and computational biology domains that provide some useful impact on intelligent data (emotional, image, etc.) analysis. They also explain how these computing technologies based data analysis approaches used for various real-life applications. The book will be beneficial for readers working in this area.

The book is a collection of peer-reviewed best selected research papers presented at the International Conference on Data Analytics and Insights (ICDAI 2024), organized by Techno International, Kolkata, India, during July 25–27, 2024. The two volumes of the book cover important topics like sensor and network data analytics and insights; big data analytics and insights; biological and biomedical data analysis and insights; optimization techniques, time series analysis and forecasting; power and energy systems data analytics and insights; civil and environmental data analytics and insights; and industry and applications.

The book is a collection of peer-reviewed best selected research papers presented at the International Conference on Data Analytics and Insights (ICDAI 2024), organized by Techno International, Kolkata, India, during July 25-27, 2024. The two volumes of the book cover important topics like sensor and network data analytics and insights; big data analytics and insights; biological and biomedical data analysis and insights; optimization techniques, time series analysis and forecasting; power and energy systems data analytics and insights; civil and environmental data analytics and insights; and industry and applications.

The book is a collection of peer-reviewed best selected research papers presented at the International Conference on Data Analytics and Insights (ICDAI 2024), organized by Techno International, Kolkata, India, during July 25–27, 2024. The two volumes of the book cover important topics like sensor and network data analytics and insights; big data analytics and insights; biological and biomedical data analysis and insights; optimization techniques, time series analysis and forecasting; power and energy systems data analytics and insights; civil and environmental data analytics and insights; and industry and applications.

The book is a collection of peer-reviewed best selected research papers presented at the International Conference on Data Analytics and Insights (ICDAI 2024), organized by Techno International, Kolkata, India, during July 25-27, 2024. The two volumes of the book cover important topics like sensor and network data analytics and insights; big data analytics and insights; biological and biomedical data analysis and insights; optimization techniques, time series analysis and forecasting; power and energy systems data analytics and insights; civil and environmental data analytics and insights; and industry and applications.

This book explores intricate world of data classification with 'Multi-Objective, Multi-Class, and Multi-Label Data Classification.' This book studies sophisticated methods and strategies for working with complicated data sets, tackling the difficulties of various classes, many objectives, and complicated labelling tasks. This resource fosters a deeper grasp of multi-dimensional data analysis in today's data-driven world by providing readers with the skills and insights needed to navigate the subtleties of modern classification jobs, from algorithmic techniques to practical applications.