Essential Concepts And Techniques Of Ai Ml
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Essential Concepts and Techniques of AI & ML
Author: Jagadish A N
language: en
Publisher: Academic Guru Publishing House
Release Date: 2024-08-14
“Essential Concepts and Techniques of AI & ML” is a comprehensive textbook designed to demystify the complexities of Artificial Intelligence and Machine Learning for learners at all levels. The book covers a broad spectrum of topics, starting with an overview of the history and evolution of AI and ML, and progressing to advanced techniques and applications. Readers will explore key concepts such as supervised and unsupervised learning, neural networks, data preprocessing, and model evaluation. Each chapter is carefully structured to provide a balance between theory and practice, with numerous examples, illustrations, and hands-on exercises. The book also delves into the ethical considerations surrounding AI and ML, ensuring that readers are aware of the broader implications of these technologies. Additionally, it introduces popular tools and frameworks, offering practical guidance on how to implement AI and ML models. Whether you are pursuing a career in AI and ML or simply want to understand the technologies driving today’s innovations, this textbook offers the essential knowledge and skills needed to navigate and contribute to this dynamic field.
Artificial Intelligence and Machine Learning in Health Care and Medical Sciences
This open access book provides a detailed review of the latest methods and applications of artificial intelligence (AI) and machine learning (ML) in medicine. With chapters focusing on enabling the reader to develop a thorough understanding of the key concepts in these subject areas along with a range of methods and resulting models that can be utilized to solve healthcare problems, the use of causal and predictive models are comprehensively discussed. Care is taken to systematically describe the concepts to facilitate the reader in developing a thorough conceptual understanding of how different methods and resulting models function and how these relate to their applicability to various issues in health care and medical sciences. Guidance is also given on how to avoid pitfalls that can be encountered on a day-to-day basis and stratify potential clinical risks. Artificial Intelligence and Machine Learning in Health Care and Medical Sciences: Best Practices and Pitfallsis a comprehensive guide to how AI and ML techniques can best be applied in health care. The emphasis placed on how to avoid a variety of pitfalls that can be encountered makes it an indispensable guide for all medical informatics professionals and physicians who utilize these methodologies on a day-to-day basis. Furthermore, this work will be of significant interest to health data scientists, administrators and to students in the health sciences seeking an up-to-date resource on the topic.
Artificial Intelligence and Machine Learning Essentials
Author: Kiran Kumar Pappula
language: en
Publisher: Academic Guru Publishing House
Release Date: 2025-02-06
Artificial Intelligence and Machine Learning Essentials is a comprehensive guide tailored for beginners and early-stage learners eager to explore the fascinating world of Al and ML. The book covers key concepts, techniques, and tools across eight well-structured chapters, offering readers a clear pathway from fundamental understanding to practical knowledge. Beginning with the basics of Artificial Intelligence, the book introduces readers to its history, types, and applications across different industries. It then delves into the core principles of Machine Learning, detailing the various types, algorithms, and workflows essential for building intelligent systems. Readers will gain insights into critical data preprocessing techniques that ensure high-quality input for ML models. The book further explores popular supervised and unsupervised learning algorithms, including linear regression, decision trees, K-means, and PCA, making it easier to grasp both the theoretical and practical aspects. Reinforcement Learning, Deep Learning models like CNNs and RNNs, and Natural Language Processing techniques are also thoroughly explained with real-life relevance. Written in simple and accessible language, the book makes complex topics easy to understand, making it suitable for university students, tech enthusiasts, and professionals from non-technical backgrounds. With a strong emphasison clarity and practical understanding, this book serves as a stepping stone into one of the most promising areas of modern technology.