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The AI Revolution: How Machines Are Changing the World

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The AI Revolution: How Machines Are Changing the World

By: Ajit Singh
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"The AI Revolution: How Machines Are Changing the World" is a comprehensive yet exceptionally accessible guide designed for the undergraduate (B.Tech) and postgraduate (M.Tech) students of computer science, information technology, and other engineering disciplines. The book serves as a one-stop resource that systematically covers the entire spectrum of Artificial Intelligence, from its foundational concepts to the most advanced, cutting-edge topics like Generative AI.


Key Features:


1. Lucid and Simple Language: Complex jargon and mathematical notations are broken down and explained using simple language and relatable analogies, making the book accessible to students from diverse backgrounds.
2. Progressive and Logical Structure: The book is organized into 10 cohesive chapters that guide the reader on a logical path from the history of AI, through machine learning, deep learning, NLP, and finally to the latest in generative AI and ethical governance.
3. Abundant Practical Examples: Every concept is illustrated with practical examples drawn from everyday life and industry, such as e-commerce recommendations, social media sentiment analysis, medical imaging, and more.
4. Hands-On with Python: Essential algorithms are accompanied by simplified Python code snippets (using popular libraries like Scikit-learn, TensorFlow, and PyTorch) to bridge the gap between theory and implementation.
5. Focus on Cutting-Edge Topics: Includes dedicated, up-to-date chapters on Computer Vision, NLP with Transformers, and the booming field of Generative AI, including Large Language Models (LLMs) and Prompt Engineering.
6. Emphasis on AI Ethics: A dedicated chapter explores the critical issues of bias, fairness, accountability, and the societal impact of AI, preparing students to be responsible technologists.
7. Capstone Project for Real-World Experience: The final chapter is a complete, easy-to-follow capstone project (Sentiment Analysis) that walks the student through the entire lifecycle of building and deploying a real AI application, solidifying their learning.


The core philosophy of this book is to build strong intuition before delving into mathematical rigor. We believe that the best way to learn AI is by understanding the simple, elegant ideas behind the algorithms and then applying them to solve tangible, real-world problems. This book departs from the traditional, theory-heavy approach and instead adopts a narrative style, rich with analogies, practical examples, and step-by-step explanations that make even the most complex topics feel intuitive and approachable. Computer Science
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