Explainable Artificial Intelligence (XAI) addresses the growing need for transparency and interpretability in AI systems, enabling trust and accountability in decision-making processes. This book offers a comprehensive guide to XAI, bridging foundational concepts with advanced methodologies. It explores interpretability in traditional models such as Decision Trees, Linear Regression, and Support Vector Machines, alongside the challenges of explaining deep learning architectures like CNNs, RNNs, and Large Language Models (LLMs), including BERT, GPT, and T5. The book presents practical techniques such as SHAP, LIME, Grad-CAM, counterfactual explanations, and causal inference, supported by Python code examples for real-world applications. Case studies illustrate XAI's role in healthcare, finance, and policymaking, demonstrating its impact on fairness and decision support. The book also covers evaluation metrics for explanation quality, an overview of cutting-edge XAI tools and frameworks, and emerging research directions, such as interpretability in federated learning and ethical AI considerations. Designed for a broad audience, this resource equips readers with the theoretical insights and practical skills needed to master XAI. Hands-on examples and additional resources are available at the companion GitHub repository: https://github.com/Echoslayer/XAI_From_Classical_Models_to_LLMs.

With the ever-evolving technological landscape, business needs and outcomes no longer exist as a default. Organizations across industries are adopting artificial intelligence (AI) systems to solve complex business problems, design intelligent and self-sustaining solutions and, essentially, stay competitive at all times. To this end, continued efforts are being made to reinvent AI systems so that more can be achieved with less. Adaptive AI is a key step in that direction. The reason why it could outpace traditional machine learning (ML) models in the near future is for its potential to empower businesses in achieving better outcomes while investing less time, effort and resources.