Summary

Machine learning is everywhere, yet rarely understood. In this episode of A Beginner’s Guide to AI, we strip away the hype and explain how machine learning actually works, why it’s so powerful, and where it quietly goes wrong.

You’ll learn how machines are trained on data rather than rules, why predictions are not understanding, and how real-world systems can produce unfair outcomes even when they look accurate. A real healthcare case shows how a cost-based algorithm systematically underestimated medical need, revealing the hidden dangers of proxy metrics.

This episode covers machine learning basics, ethical AI, algorithmic bias, fairness, and transparency in a way that is accessible to beginners and useful for professionals.

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Quotes from the Episode

• “Machine learning gives you what you measure, not what you value.”
• “The algorithm didn’t invent bias. It learned it efficiently.”
• “A perfect prediction of the wrong thing is still failure.”

Chapters

00:00 Machine Learning Without the Myth

04:12 How Machines Learn From Data

10:45 Types of Machine Learning

18:30 The Cake Example

26:05 Healthcare Case Study

36:40 Ethics, Bias, and Proxies

45:50 Final Takeaways

About Dietmar Fischer:

Dietmar is a podcaster and AI marketer from Berlin. If you want to know how to get your AI or your digital marketing going, just contact him.

Music credit: Modern Situations by Unicorn Heads

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