Rajendra Akerkar Livres

Covering both fundamental and advanced topics in Web operations, this reference integrates essential concepts with the latest trends. It delves into areas such as agent-based Web, wrapper induction, Web mining, information retrieval, and social networks. The book features numerous examples and over 100 illustrations to simplify complex material. Additionally, it includes bibliographical notes, end-of-chapter exercises, glossaries, and practice questions with solutions to enhance learning and understanding.

Focusing on practical applications, this guide explores artificial intelligence techniques relevant to business without delving into complex algorithms. It introduces AI concepts, machine learning workflows, and deep learning, while also covering recommendation engines and natural language processing for sentiment analysis. Each chapter builds on the last, helping students and executives understand how to leverage AI to enhance customer experience and improve competitive positioning. The final chapter discusses future business prospects and the benefits of AI integration.

The book explores the transformative impact of big data on industrial and academic research, emphasizing the necessity for a paradigm shift in algorithms and mathematical techniques. It highlights the challenges posed by large data sets that exceed memory capacity, advocating for innovative algorithmic approaches to effectively handle these situations. By addressing foundational strengths and contemporary challenges, the book aims to provide practical solutions for leveraging big data in various applications.

The main objective is to provide quick and essential knowledge for the subject with the help of summary and solved questions /case studies without going into detailed discussion.

This textbook provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods. These embrace the family of neural networks, fuzzy systems and evolutionary computing in addition to other fields within machine learning, and will help in identifying, visualizing, classifying and analyzing data to support business decisions./p> The authors, discuss advantages and drawbacks of different approaches, and present a sound foundation for the reader to design and implement data analytic solutions for real‐world applications in an intelligent manner. Intelligent Techniques for Data Science also provides real-world cases of extracting value from data in various domains such as retail, health, aviation, telecommunication and tourism.