Artificial Intelligence for Data Science in Theory and Practice

This book provides valuable information on effective, state-of-the-art techniques and approaches for governments, students, researchers, practitioners, entrepreneurs, and teachers in the field of artificial intelligence (AI). It explains the data and AI, types and properties of data, the relation between AI algorithms and data, what makes data AI-ready, steps of data pre-processing, data quality, data storage, and data platforms.

Format: Paperback / softback
Length: 246 pages
Publication date: 07 April 2023
Publisher: Springer Nature Switzerland AG

This comprehensive book offers invaluable insights and cutting-edge techniques for governments, students, researchers, practitioners, entrepreneurs, and teachers in the realm of artificial intelligence (AI). It delves into the intricate world of data and AI, exploring various aspects such as data types and properties, the relationship between AI algorithms and data, the criteria for making data AI-ready, the processes involved in data pre-processing, data quality assurance, data storage solutions, and data platforms.

With its extensive coverage, this book appeals to a wide range of AI practitioners, academics, researchers, and lecturers specializing in computer science, artificial intelligence, machine learning, and data sciences. It serves as a valuable resource for anyone seeking to stay updated with the latest advancements and applications of AI in today's rapidly evolving landscape.

The book begins by providing an overview of the fundamental concepts and principles of AI, including machine learning, natural language processing, and computer vision. It then delves into the realm of data, emphasizing its importance in driving AI advancements. The authors explain the different types of data and their characteristics, highlighting the challenges and opportunities associated with each.

The relationship between AI algorithms and data is explored in depth, with a focus on how data can be used to train and improve AI models. The book discusses the various algorithms and techniques used in data pre-processing, such as data cleaning, transformation, and feature extraction, to ensure that the data is suitable for AI analysis.

Data quality is a critical aspect of AI, and the book provides comprehensive guidelines for assessing and improving data quality. It discusses the importance of data representation, accuracy, consistency, and completeness, and outlines strategies for identifying and addressing data quality issues.

Data storage and data platforms are also covered in detail, with a focus on the latest technologies and solutions for managing and analyzing large datasets. The book discusses the advantages and disadvantages of different storage systems, such as cloud storage, relational databases, and NoSQL databases, and highlights the importance of choosing the right platform for specific AI applications.

Throughout the book, real-world examples and case studies are used to illustrate the practical applications of AI and data. These examples showcase the successful integration of AI and data in various industries, including healthcare, finance, transportation, and marketing.

In conclusion, this book is a must-read for anyone interested in AI and data. It provides a comprehensive and up-to-date understanding of the latest techniques and approaches for leveraging AI and data to solve real-world problems. Whether you are a government official, a student, a researcher, a practitioner, or a teacher, this book will help you gain a deeper appreciation of the potential of AI and data in shaping our future.

Weight: 409g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030922474
Edition number: 1st ed. 2022