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ML-For-Beginners
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@ -82,7 +82,7 @@ By ensuring that the content aligns with projects, the process is made more enga
| 19 | Introduction to Time Series Forecasting | [Time Series]() | tbd | [lesson]() | Francesca |
| 20 | Power Usage in India ⚡️ | [Time Series]() | tbd | [lesson]() | Francesca |
| 21 | Introduction to Reinforcement Learning | [Reinforcement Learning]() | tbd | [lesson]() | Dmitry |
| 22 | A fancy French mousetrap 🍫 | [Reinforcement Learning]() | tbd | [lesson]() | Dmitry |
| 22 | Help Peter avoid the Wolf! 🐺 | [Reinforcement Learning]() | tbd | [lesson]() | Dmitry |
| 23 | Real-World ML Scenarios and Applications | The Ethics of Machine Learning | What are the important ethical issues apparent now and how will they impact the field going forward? | [lesson](8-Real-World/1-Ethics/README.md) | Tomomi |
| 24 | Real-World ML Scenarios and Applications | The Future of Machine Learning | Interesting and Revealing real-world applications of ML | [lesson](8-Real-World/2-Applications/README.md) | All |
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