AI Engineering

Key Features of the Book:
1. Practical-First Approach: Saturated with the simplest and easiest practical examples, code walkthroughs, and real-world datasets to bridge the gap between theory and application.
2. End-to-End AI Engineering Lifecycle: The only textbook at this level to dedicate chapters to the crucial areas of MLOps (Model Lifecycle Management) and Responsible AI (Explainability, Fairness, Bias).
3. Simplified Mathematics: Complex mathematical derivations are replaced with intuitive explanations, focusing on how to use mathematical tools to solve problems.
4. Integrated Capstone Project: A full chapter dedicated to a real-world project-Customer Churn Prediction-from data gathering and cleaning to building, explaining, and deploying a model as a web app.
5. Cutting-Edge Content: Includes up-to-date and relevant topics like Transformers, Generative AI (GANs, LLMs), and Explainable AI (LIME, SHAP), which are essential for today's job market.
6. Global and Indian Context: While meeting international standards, the examples and case studies are chosen to be relatable to students everywhere, including the Indian context.
My core philosophy is "Simplicity in Theory, Rigor in Practice." Complex mathematical concepts are demystified and presented with an intuitive, application-oriented approach. Every theoretical concept is immediately followed by a simple, easy-to-understand practical example using popular Python libraries. This ensures that students can solidify their understanding by implementing the concepts themselves.

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