Artificial Intelligence and Machine Learning for Real-World Applications : A Beginner's Guide with Case Studies

個数：

予約

Artificial Intelligence and Machine Learning for Real-World Applications : A Beginner's Guide with Case Studies

Malik, Latesh/ Arora, Sandhya/ Shrawankar, Urmila

ウェブストア価格 ¥17,231（本体¥15,665）
Chapman & Hall/CRC（2025/10発売）
外貨定価 US$ 84.99
ポイント 156pt

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製本 Hardcover:ハードカバー版／ページ数 256 p.
言語 ENG
商品コード 9781032873466

Full Description

This book introduces foundational and advanced concepts in artificial intelligence and machine learning, focusing on their real-world applications and societal implications. Covering topics from knowledge representation and model interpretability to deep learning and Generative AI, it includes practical Python implementations and case studies from healthcare, agriculture, and education. Beginning with core concepts such as AI fundamentals, knowledge representation, and statistical techniques, it gradually advances to cover machine learning algorithms, deep learning architectures, and the basics of Generative AI. Detailed discussions of data preprocessing, model training, evaluation metrics, and Python-based implementation make this book both practical and accessible.

Offers real-world examples and case studies illustrating the societal impact and practical applications of AI and ML technologies
Discusses data pre-processing techniques, model selection, and evaluation metrics, with practical implementation in Python and in detail
Explores AI problem-solving processes, knowledge representation, and model training strategies, catering to readers with varying levels of technical expertise
Covers AI and ML principles, spanning statistical techniques, machine learning algorithms, deep learning structures and Generative AI basics.
Focuses on societal applications in healthcare, agriculture, and education, addressing challenges faced by elderly and special needs individuals

This book is for professionals, researchers and scholars interested in the application of artificial intelligence and machine learning.

Preface

Acknowledgements

Author biography

1. Introduction to Artificial Intelligence and Machine Learning 2. Problem Solving Methods and Search Strategies 3. Knowledge Representation 4. Machine Learning, Data and Preprocessing 5. Supervised Learning 6: Unsupervised Machine Learning 7. Neural Networks and Deep Learning 8. Generative Artificial Intelligence 9. AI in healthcare: Diagnostics, Treatment, and Beyond 10. AI and ML for agriculture developments 11. AI Transforming Education: Personalized Learning and Intelligent Tutoring Systems 12. Technological uses of AL ML for helping elderly and special needs people