This book begins with the basics of AI, explaining ML and design patterns to build a solid foundation. It delves deeply into generative AI and then progresses through machine learning, deep learning, and essential architectures such as CNNs, GANs, Diffusion, RNNs, LSTMs, and Transformers. It covers practical applications, from regression and classification to advanced use cases such as image generation, editing, document search, content summarization, and question answering. Readers will also learn to build prototypes like a Document Q&A bot, research assistant, and prompt playground, while mastering techniques such as continued pre-training, fine-tuning, model merging, retrieval-augmented generation, and agentic AI.