Integrating machine learning with differential privacy involves incorporating privacy-preserving mechanisms into the model training process. One common method is differentially private stochastic gradient descent (DPSGD), where noise is added to the gradients during the training of a machine learning model. This ensures that the final model, although trained on sensitive data, does not reveal specific details about individual data entries. The challenge lies in balancing the amount of noise added (to ensure privacy) with the accuracy of the model. This approach is crucial in fields like healthcare and finance, where models need to be trained on sensitive data without compromising individual privacy.