Can small datasets still be useful for ML?
Not every company has big data. Is it possible to build effective models with limited data, and what techniques help in such cases?
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Working with small datasets is challenging, but not impossible. Techniques like transfer learning and data augmentation can help improve model performance even with limited data. However, one must be careful about bias in machine learning, which can become more significant when datasets are small and not representative of real-world scenarios.