Artificial Intelligence for Absolute Beginners
From Rule-Based Systems to Modern AI Agents
ChatGPT. Self-driving cars. AI that writes code. Ever wonder what's actually happening under the hood?
Artificial Intelligence for Absolute Beginners is Book 3 of the Data Science Foundations Series — a clear-eyed, jargon-free guide to how modern AI actually works, from rule-based systems to today's large language models and AI agents.
Inside, you'll learn how to:
• Understand the real difference between AI, machine learning, and deep learning (most people get this wrong)
• Trace AI's evolution from symbolic logic and expert systems to neural networks and transformers
• Understand how large language models like GPT actually generate text, token by token
• Explore computer vision, natural language processing, and reinforcement learning with real examples
• Separate genuine AI capability from hype, marketing, and science fiction
• Think critically about AI ethics, bias, and the genuine risks and limits of today's systems
• Get a clear, honest look at where AI is realistically headed next
Why this book is different:
This isn't a buzzword tour. Every concept is grounded in real code, real datasets, and real worked examples using Python and scikit-learn — so you don't just learn AI vocabulary, you learn how the underlying systems genuinely work. Complex ideas like attention mechanisms and neural architectures are explained step by step, with honesty about what current AI can and cannot do.
Perfect for curious beginners, students, professionals navigating an AI-driven workplace, and anyone tired of AI explainers that are either too dumbed-down or too dense to follow.
No programming or technical background required to understand the core ideas — though readers comfortable with Python will get even more from the hands-on examples.
Datasets used throughout this book are available for free download at www.waskeypress.com.
Reader reviews (1)
Great book.I thought I already understood AI after messing around with ChatGPT for months, but this book showed me how shallow that understanding actually was. What struck me most is that it doesn't jump straight to LLMs , it starts with rule-based systems and search, so you actually understand where all of this came from and why language models work the way they do. The prompt engineering chapter is concrete, not just a vague list of tips, and the AI agents chapter walked me through building my first agent that actually uses tools. The chapter on ethics and bias felt honest too, not just an obligatory paragraph tacked on at the end. Great if you want to move past being just a 'ChatGPT user.
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