1. A method of training a machine deep learning system, using a network computer, comprising: when a classification result score is less than a threshold value, iteratively performing further actions, including: randomly selecting a batch of data items from unlabeled test data; providing a classification of the batch of data items using the machine deep learning system, wherein the machine deep learning system is partially trained by one or more previously labeled data items; communicating the batch of classified data items to a client computer; labeling one or more classified data items based on content of the one or more classified data items; communicating the one or more labeled data items to the machine deep learning system; employing the one or more labeled data items to further train the machine deep learning system; and updating the classification result score based on a count of the one or more labeled data items that are classified correctly by the machine deep learning system; and when the classification result score at least meets the threshold value, employing the machine deep learning system to classify live data.