Overview
- Unique, understandable view of machine learning using many practical examples and access to open source code
- Introduces AI-TOOLKIT, freely available software that allows the reader to test and study the examples in the book
- No programming or scripting skills needed
- Suitable for self-study by professionals, also useful as a supplementary resource for advanced undergraduate and graduate courses on AI
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Table of contents (9 chapters)
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Front Matter
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Introduction
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Front Matter
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An In-Depth Overview of Machine Learning
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Front Matter
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Automatic Speech Recognition
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Front Matter
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Biometrics Recognition
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Front Matter
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Machine Learning by Example
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Front Matter
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The AI-TOOLKIT Machine Learning Made Simple
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Front Matter
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Back Matter
Keywords
About this book
This book presents a unique, understandable view of machine learning using many practical examples and access to free professional software and open source code. The user-friendly software can immediately be used to apply everything you learn in the book without the need for programming.
After an introduction to machine learning and artificial intelligence, the chapters in Part II present deeper explanations of machine learning algorithms, performance evaluation of machine learning models, and how to consider data in machine learning environments. In Part III the author explains automatic speech recognition, and in Part IV biometrics recognition, face- and speaker-recognition. By Part V the author can then explain machine learning by example, he offers cases from real-world applications, problems, and techniques, such as anomaly detection and root cause analyses, business process improvement, detecting and predicting diseases, recommendation AI, several engineering applications, predictive maintenance, automatically classifying datasets, dimensionality reduction, and image recognition. Finally, in Part VI he offers a detailed explanation of the AI-TOOLKIT, software he developed that allows the reader to test and study the examples in the book and the application of machine learning in professional environments.
The author introduces core machine learning concepts and supports these with practical examples of their use, so professionals will appreciate his approach and use the book for self-study. It will also be useful as a supplementary resource for advanced undergraduate and graduate courses on machine learning and artificial intelligence.
Authors and Affiliations
About the author
Bibliographic Information
Book Title: The Application of Artificial Intelligence
Book Subtitle: Step-by-Step Guide from Beginner to Expert
Authors: Zoltán Somogyi
DOI: https://doi.org/10.1007/978-3-030-60032-7
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-60031-0Published: 12 March 2021
Softcover ISBN: 978-3-030-60034-1Published: 13 March 2022
eBook ISBN: 978-3-030-60032-7Published: 11 March 2021
Edition Number: 1
Number of Pages: XXXV, 431
Number of Illustrations: 75 b/w illustrations, 228 illustrations in colour
Topics: Artificial Intelligence, Machine Learning, Data Structures and Information Theory