Techtalks: Traveltech

The amount of training data required for machine learning can vary depending on several factors, including the complexity of the task, the complexity of the model, and the quality of the data. In general, having more diverse and representative training data tends to improve the performance of machine learning models.

However, there is no fixed rule for the exact amount of training data needed. Some machine learning algorithms can achieve reasonable performance with a few hundred or thousand labeled examples, while others may require millions of examples. It also depends on the dimensionality of the data and the complexity of the patterns to be learned.