Methods and systems are presented for providing a semi-supervised machine learning framework for training a machine learning model using partly mislabeled training data sets. Using the semi-supervised machine learning framework, an iterative training process is performed on the machine learning model, wherein the training data is being adjusted continuously in each iteration for training the machine learning model. During each iteration, the machine learning model is evaluated based on its ability to identify training data that has been mislabeled. The labeling of identified mislabeled training data is corrected before feeding back to the machine learning model in the next training iteration.