The present book is primarily intended for undergraduate and postgraduate students of computer science and engineering, information technology, and electrical and electronics engineering. It bridges the gaps in knowledge of the seemingly difficult areas of machine learning and nature inspired computing. The text is written in a highly interactive manner, which satisfies the learning curiosity of any reader. Content of the text has been diligently organized to offer seamless learning experience. The text begins with introduction to machine learning, which is followed by explanation of different aspects of machine learning. Various supervised, unsupervised, reinforced and nature inspired learning techniques are included in the text book with numerous examples and case studies. Different aspects of new machine learning and nature inspired learning algorithms are explained in-depth. The well-explained algorithms and pseudo codes for each topic make this book useful for students. The book also throws light on areas like prediction and classification systems. Key Features • Day to day examples and pictorial representations for deeper understanding of the subject • Helps readers easily create programs/applications • Research oriented approach • More case studies and worked-out examples for each machine learning algorithm than any other book
Primarily intended for the undergraduate and postgraduate students of computer science and engineering, this textbook (earlier titled as Artificial Intelligence and Machine Learning), now in its second edition, bridges the gaps in knowledge of the seemingly difficult areas of artificial intelligence. This book promises to provide the most number of case studies and worked-out examples among the books of its genre. The text is written in a highly interactive manner which fulfils the curiosity of any reader. Moreover, the content takes off from the introduction to artificial intelligence, which is followed by explaining about intelligent agents. Various problem-solving strategies, knowledge representation schemes are also included with numerous case studies and applications. Different aspects of learning, nature-inspired learning, along with natural language processing are also explained in depth. The algorithms and pseudo codes for each topic make this book useful for students. Book also throws light into areas like planning, expert system and robotics. Book concludes with futuristic artificial intelligence, which explains the fascinating applications, that the world will witness in coming years. KEY FEATURES • Day-to-day examples and practical representations for deeper understanding of the subject. • Learners can easily implement the AI applications. • Effective and useful case studies and worked-out examples for AI problems. Target Audience • Students of B.E./B.Tech Computer Science Engineering • Students of M.E./M.Tech Computer Science Engineering
Primarily intended for the undergraduate and postgraduate students of computer science and engineering, this textbook (earlier titled as Artificial Intelligence and Machine Learning), now in its second edition, bridges the gaps in knowledge of the seemingly difficult areas of artificial intelligence. This book promises to provide the most number of case studies and worked-out examples among the books of its genre. The text is written in a highly interactive manner which fulfils the curiosity of any reader. Moreover, the content takes off from the introduction to artificial intelligence, which is followed by explaining about intelligent agents. Various problem-solving strategies, knowledge representation schemes are also included with numerous case studies and applications. Different aspects of learning, nature-inspired learning, along with natural language processing are also explained in depth. The algorithms and pseudo codes for each topic make this book useful for students. Book also throws light into areas like planning, expert system and robotics. Book concludes with futuristic artificial intelligence, which explains the fascinating applications, that the world will witness in coming years. KEY FEATURES • Day-to-day examples and practical representations for deeper understanding of the subject. • Learners can easily implement the AI applications. • Effective and useful case studies and worked-out examples for AI problems. Target Audience • Students of B.E./B.Tech Computer Science Engineering • Students of M.E./M.Tech Computer Science Engineering
The present book is primarily intended for undergraduate and postgraduate students of computer science and engineering, information technology, and electrical and electronics engineering. It bridges the gaps in knowledge of the seemingly difficult areas of machine learning and nature inspired computing. The text is written in a highly interactive manner, which satisfies the learning curiosity of any reader. Content of the text has been diligently organized to offer seamless learning experience. The text begins with introduction to machine learning, which is followed by explanation of different aspects of machine learning. Various supervised, unsupervised, reinforced and nature inspired learning techniques are included in the text book with numerous examples and case studies. Different aspects of new machine learning and nature inspired learning algorithms are explained in-depth. The well-explained algorithms and pseudo codes for each topic make this book useful for students. The book also throws light on areas like prediction and classification systems. Key Features • Day to day examples and pictorial representations for deeper understanding of the subject • Helps readers easily create programs/applications • Research oriented approach • More case studies and worked-out examples for each machine learning algorithm than any other book
This book offers a standardized approach for research aspirants working in the various areas. At the same time, all the major topics in social research have also been detailed thoroughly which makes this book a very good frame of study for students and researchers in diverse fields. This book charts new and evolving terrain of social research by covering qualitative, quantitative and mixed approach. The chapters has extensive number of case studies that help researchers to understand practical implications of the research and includes plenty of diagrammatic representations for easy understanding of various theories and procedures. Each phase of research is explained in detail so that even beginners can also effectively utilize this book. It is written in a highly interactive manner, which makes for an interesting read. Templates of technical report, business report and research reports are also included in the book. This provides the reader with a hands-on experience.
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