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32 lines
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32 lines
2.0 KiB
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# Postscript: Real-world applications of classical machine learning
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In this section of the curriculum, you'll explore some practical applications of classical machine learning. We've searched extensively to find whitepapers and articles showcasing how these techniques are applied, while deliberately minimizing the focus on neural networks, deep learning, and AI. Discover how machine learning is utilized in business systems, environmental projects, finance, arts and culture, and more.
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> Photo by <a href="https://unsplash.com/@childeye?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Alexis Fauvet</a> on <a href="https://unsplash.com/s/photos/artificial-intelligence?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
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## Lesson
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1. [Real-World Applications for ML](1-Applications/README.md)
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2. [Model Debugging in Machine Learning using Responsible AI dashboard components](2-Debugging-ML-Models/README.md)
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## Credits
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"Real-World Applications" was created by a team including [Jen Looper](https://twitter.com/jenlooper) and [Ornella Altunyan](https://twitter.com/ornelladotcom).
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"Model Debugging in Machine Learning using Responsible AI dashboard components" was authored by [Ruth Yakubu](https://twitter.com/ruthieyakubu)
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---
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**Disclaimer**:
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This document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we strive for accuracy, please note that automated translations may contain errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is recommended. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation. |