Free ebook introducing AI concepts, machine learning use cases, generative AI, data, risks, and essential terminology for absolute beginners.
Free ebook content
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What AI Is and What It Is Not
+ Exercise: Which statement best matches how AI differs from traditional rule-based software?
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Data as the Fuel: Examples, Labels, and Quality
+ Exercise: Which situation best illustrates data leakage that can make a model seem better in testing than it will be in real use?
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Models as Pattern Finders: Simple Mental Models and Analogies
+ Exercise: Why can an AI model produce a high confidence score and still be wrong?
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Training vs. Inference: Learning Compared to Using What Was Learned
+ Exercise: Which situation best describes inference rather than training in an AI system?
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Supervised Learning: Learning from Labeled Examples
+ Exercise: Why can a supervised learning model perform well during testing but fail after deployment?
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Unsupervised Learning: Discovering Groups and Structure Without Labels
+ Exercise: Why is scaling numeric features often an important preprocessing step before running a clustering algorithm?
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Core Use Cases: Prediction, Classification, Clustering, and Recommendation
+ Exercise: A team wants an AI feature that shows each user a top 10 list of items they are most likely to engage with right now. Which core AI use case best matches this output?
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Generative AI Basics: Creating Text, Images, and Audio from Patterns
+ Exercise: Which prompt revision best reduces the risk of the model inventing product details while keeping the output easy to check?
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How to Evaluate AI Demos and Claims Without Coding
+ Exercise: When evaluating an AI demo without coding, which approach best helps determine whether the system will work in your real environment?
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Limits, Risks, and Responsible Use: Bias, Privacy, and Reliability
+ Exercise: Which practice best reduces privacy risk when using an AI assistant to draft a customer email at work?
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Essential AI Glossary: Key Terms You Will Hear Often
+ Exercise: Which situation best indicates that an AI tool is using RAG to answer questions about your uploaded documents?
About the free ebook
AI Fundamentals for Absolute Beginners
This free ebook introduces artificial intelligence in clear, practical language for readers who want to understand the technology without needing to code. Learn what AI can do, what it cannot reliably do, and why data quality matters when systems learn from examples.
Build a practical mental model of AI
Explore how models identify patterns, how training differs from inference, and how labeled and unlabeled data support different kinds of machine learning. Simple explanations connect core ideas to familiar real-world situations.
Recognize common AI applications
Understand the difference between prediction, classification, clustering, recommendation, and generative AI. The ebook explains how AI can produce text, images, and audio from learned patterns while highlighting the importance of checking outputs.
Assess AI responsibly
Learn how to evaluate AI demonstrations and marketing claims without technical tools. Consider key limitations involving reliability, bias, privacy, and appropriate human oversight.
Use essential AI vocabulary with confidence
Gain a foundation in the terms commonly used in AI and machine learning conversations, helping you read product descriptions, follow workplace discussions, and ask better questions about AI systems.
- Clear, non-technical explanations
- Everyday examples of machine learning concepts
- Guidance for evaluating AI results and risks
What is the difference between AI training and inference?
Training is when a model learns patterns from data; inference is when it uses those learned patterns to produce an output.
How does supervised learning differ from unsupervised learning?
Supervised learning uses labeled examples, while unsupervised learning finds groups or patterns in data without labels.
What should I check before trusting an AI-generated answer?
Check the source, accuracy, missing context, possible bias, and whether sensitive data was used.
This ebook includes:
11 content chapters
Digital certificate of course completion (Free)
Exercises to train your knowledge
100% free, from content to certificate
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