Machine learning algorithms are often categorized as Supervised or UN-Supervised. Supervised algorithms require a Data Scientist or data analyst with machine learning skills to provide both input and desired output, in addition to furnishing feedback about the accuracy of predictions during algorithm training. Data scientists determine which variables, or features, the model should analyze and use to develop predictions. Once training is complete, the algorithm will apply what was learned to new data.
Unsupervised algorithms do not need to be trained with desired outcome data. Instead, they use an iterative approach called Deep Learning to review data and arrive at conclusions. Unsupervised learning algorithms — also called neural network– are used for more complex processing tasks than supervised learning systems, including image recognition, speech-to-text and natural language generation. These neural networks work by combing through millions of examples of training data and automatically identifying often subtle correlations between many variables. Once trained, the algorithm can use its bank of associations to interpret new data. These algorithms have only become feasible in the age of big data, as they require massive amounts of training data.