Overview

In an age where machine learning makes decisions about healthcare, finance, hiring, and justice, transparency matters more than ever. Explainable AI for Beginners is your friendly, step-by-step guide to building AI models that are not just accurate, but also clear, interpretable, and trustworthy. Written in plain English, this book cuts through the jargon to show you how to: Understand the fundamentals of Explainable AI (XAI) and why it's essential for fairness, safety, and accountability. Build simple, interpretable models using decision trees, linear models, and rule-based systems. Use practical XAI techniques-like SHAP values, LIME, and feature importance-to open the ""black box"" of complex models. Balance accuracy and interpretability so you can make informed trade-offs in real projects. Communicate insights clearly to non-technical stakeholders, regulators, and clients. Through relatable examples and hands-on exercises, you'll learn how to design AI systems that you-and others-can understand and trust. No advanced math or coding background required-just curiosity and the desire to build ethical, responsible AI. If you've ever wanted to peek inside the mind of an algorithm, or make machine learning less mysterious, this is the book for you.

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