The Application of Artificial Intelligence: Step-by-Step Guide from Beginner to Expert

This book provides a comprehensive and accessible introduction to machine learning, covering algorithms, performance evaluation, data considerations, and real-world applications. It offers practical examples and access to professional software and open-source code, making it suitable for self-study and as a supplementary resource for advanced courses.

Format: Paperback / softback
Length: 431 pages
Publication date: 13 March 2022
Publisher: Springer Nature Switzerland AG

This comprehensive book offers a unique and accessible perspective on machine learning, utilizing a wealth of practical examples and providing access to free professional software and open-source code. The user-friendly software enables immediate application of the knowledge gained without the requirement for programming expertise.

After providing an introduction to machine learning and artificial intelligence, the chapters in Part II delve deeper into the explanations of machine learning algorithms, the evaluation of machine learning models' performance, and the consideration of data in machine learning environments. Part III focuses on automatic speech recognition, while Part IV explores biometrics recognition, encompassing face and speaker recognition.

In Part V, the author presents machine learning through example, offering real-world applications, problems, and techniques such as anomaly detection, root cause analyses, business process improvement, disease detection and prediction, recommendation AI, various engineering applications, predictive maintenance, automatically classifying datasets, dimensionality reduction, and image recognition. Finally, in Part VI, the author offers a detailed explanation of the AI-TOOLKIT, software he developed that empowers readers to test and study the examples in the book and apply machine learning in professional environments.

The author's approach is designed to appeal to professionals, who will appreciate its practicality and use the book for self-study. Additionally, it serves as an invaluable supplementary resource for advanced undergraduate and graduate courses in machine learning and artificial intelligence.

Weight: 712g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030600341
Edition number: 1st ed. 2021