Machine Learning Algorithms and Applications: Theory and Applications

The book is written for experienced and starting machine learning specialists looking to implement solutions to real-world machine learning problems.

Machine Learning Algorithms and Applications shows how one can easily adopt machine learning to build solutions for small applications. It clearly explains the various applications of machine and deep learning for use in the medical field, animal classification, gene selection from microarray gene expression data, sentiment analysis, manufacturing, fake profile detection in social media, farming sectors, etc.

For the veteran and new machine learning specialists who are looking to implement solutions to real-world machine learning problems, this book thoroughly discusses the various applications of machine and deep learning techniques. Each chapter deals with the novel approach of machine learning architecture for a specific application and its results include comparisons with previous algorithms. In order to present a unified treatment of machine learning problems and solutions, many methods based in different fields are discussed, including statistics, pattern recognition, neural networks, artificial intelligence, sentiment analysis, control, and data mining. Furthermore, all learning algorithms are explained in a way that makes it easy for students to move from the equations in the book to a computer program

Audience The book is primarily intended for researchers, students, and professionals in computer science, information technology, cybernetics, system sciences, engineering, statistics, and social sciences, and as a reference for software professionals and practitioners in biomedical fields, manufacturing, supply chain and logistics, agriculture, and Industry 4.0 professionals.

The book is written for experienced and starting machine learning specialists looking to implement solutions to real-world machine learning problems.

Machine Learning Algorithms and Applications shows how one can easily adopt machine learning to build solutions for small applications. It clearly explains the various applications of machine and deep learning for use in the medical field, animal classification, gene selection from microarray gene expression data, sentiment analysis, manufacturing, fake profile detection in social media, farming sectors, etc.

For the veteran and new machine learning specialists who are looking to implement solutions to real-world machine learning problems, this book thoroughly discusses the various applications of machine and deep learning techniques. Each chapter deals with the novel approach of machine learning architecture for a specific application and its results include comparisons with previous algorithms. In order to present a unified treatment of machine learning problems and solutions, many methods based in different fields are discussed, including statistics, pattern recognition, neural networks, artificial intelligence, sentiment analysis, control, and data mining. Furthermore, all learning algorithms are explained in a way that makes it easy for students to move from the equations in the book to a computer program

Audience The book is primarily intended for researchers, students, and professionals in computer science, information technology, cybernetics, system sciences, engineering, statistics, and social sciences, and as a reference for software professionals and practitioners in biomedical fields, manufacturing, supply chain and logistics, agriculture, and Industry 4.0 professionals.