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: Hardback
Length: 246 pages
Publication date: 06 April 2022
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, ensuring data quality, managing data storage, and utilizing 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 data and AI. It explains the different types of data and their characteristics, emphasizing the importance of understanding the underlying structure and properties of data in order to facilitate effective AI applications. The authors then delve into the relationship between AI algorithms and data, discussing how algorithms can be designed and trained to extract valuable insights and make informed decisions based on available data.

Furthermore, the book addresses the critical aspect of making data AI-ready. It explores the steps involved in data pre-processing, which includes cleaning, transforming, and annotating data to ensure its quality and suitability for AI algorithms. The authors discuss techniques such as data augmentation, feature extraction, and dimensionality reduction, which are essential for preparing data for machine learning algorithms.

Data quality is another crucial aspect that is emphasized throughout the book. The authors discuss the importance of identifying and addressing data biases, errors, and inconsistencies, which can negatively impact the accuracy and reliability of AI systems. They provide practical guidelines and techniques for assessing data quality, including statistical analysis, visual inspection, and automated validation methods.

Data storage and management are also covered in detail. The authors discuss the various storage options available, such as cloud storage, local storage, and distributed storage systems, and their advantages and disadvantages. They also explore the challenges associated with data storage, such as scalability, security, and privacy, and provide solutions to address these concerns.

Data platforms are another important aspect of AI applications. The authors discuss the various platforms and tools available for data processing, analysis, and visualization, such as Python, R, SQL, and Tableau. They provide practical examples and tutorials to help readers understand how to use these platforms effectively and efficiently.

Throughout the book, the authors emphasize the importance of interdisciplinary collaboration and the integration of AI with other fields such as computer science, mathematics, and statistics. They highlight the significance of understanding the underlying principles and theories of AI in order to develop robust and ethical AI systems.

In conclusion, this comprehensive book offers a wealth of valuable information and insights into the world of data and AI. It is an essential resource for governments, students, researchers, practitioners, entrepreneurs, and teachers who are interested in advancing their knowledge and expertise in this field. By covering a wide range of topics and providing practical examples and tutorials, this book empowers readers to develop effective AI solutions and contribute to the growth and development of the AI industry.

Weight: 565g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030922443
Edition number: 1st ed. 2022