A multifaceted approach to develop an understanding of AI and its potential applications Key Features ? AI-informed focuses on AI foundation, applications, and methodologies. ? AI-inquired focuses on computational thinking and bias awareness. ? AI-innovate focuses on creative and critical thinking and the Capstone project. Description AI is a discipline in Computer Science that focuses on developing intelligent machines, machines that can learn and then teach themselves. If you are interested in AI, this book can definitely help you prepare for future careers in AI and related fields. The book is aligned with the CBSE course, which focuses on developing employability and vocational competencies of students in skill subjects. The book is an introduction to the basics of AI. It is divided into three parts - AI-informed, AI-inquired and AI-innovate. It will help you understand AI's implications on society and the world. You will also develop a deeper understanding of how it works and how it can be used to solve complex real-world problems. Additionally, the book will also focus on important skills such as problem scoping, goal setting, data analysis, and visualization, which are essential for success in AI projects. Lastly, you will learn how decision trees, neural networks, and other AI concepts are commonly used in real-world applications. By the end of the book, you will develop the skills and competencies required to pursue a career in AI. What you will learn ? Get familiar with the basics of AI and Machine Learning. ? Understand how and where AI can be applied. ? Explore different applications of mathematical methods in AI. ? Get tips for improving your skills in Data Storytelling. ? Understand what is AI bias and how it can affect human rights. Who this book is for This book is for CBSE class XI and XII students who want to learn and explore more about AI. Basic knowledge of Statistical concepts, Algebra, and Plotting of equations is a must. Table of Contents 1. AI for Everyone 2. AI Applications and Methodologies 3. Mathematics in Artificial Intelligence 4. AI Values (Ethical Decision-Making) 5. Introduction to Storytelling 6. Critical and Creative Thinking 7. Data Analysis 8. Regression 9. Classification and Clustering 10. AI Values (Bias Awareness) 11. Capstone Project 12. Model Lifecycle (Knowledge) 13. Storytelling Through Data 14. AI Applications in Use in Real-World