Machine learning algorithms are often categorized as supervised or unsupervised. Detected algorithms require a data scientist or data analyst with machine learning skills to provide both input and desired output, as well as providing feedback on the accuracy of predictions during algorithm training. Data scientists determine which variables or features the model will analyze and use to improve predictions. Once the training is complete, the algorithm will apply what has been learned to the 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 draw conclusions. Unsupervised learning algorithms (also called neural networks) 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 scanning millions of training data samples and automatically detecting often subtle correlations between many variables. Once trained, the algorithm can use the database to interpret new data. These algorithms have only become viable in the age of big data, as they require large amounts of training data.