This book introduces foundational and advanced concepts in artificial intelligence (AI) and machine learning (ML), focusing on their real-world applications and societal implications. Covering topics from knowledge representation and model interpretability to deep learning and generative AI, Artificial Intelligence and Machine Learning for Real-World Applications: A Beginner's Guide with Case Studies 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, the text gradually advances to cover ML 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 preprocessing 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, ML algorithms, deep learning structures, and generative AI basics Focuses on societal applications in healthcare, agriculture, and education, addressing challenges faced by the elderly and special needs individuals This book is for professionals, researchers, and scholars interested in the application of AI and ML.
