Machine learning (ML) is a category of algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed.
The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output while updating outputs as new data becomes available.
The processes involved in machine learning are similar to that of data mining and predictive modeling.
Both require searching through data to look for patterns and adjusting program actions accordingly.
Many people are familiar with machine learning from shopping on the internet and being served ads related to their purchase.
This happens because recommendation engines use machine learning to personalize online ad delivery in almost real time.
Beyond personalized marketing, other common machine learning use cases include fraud detection, spam filtering, network security threat detection, predictive maintenance and building news feeds.
Machine learning algorithms are often categorized as supervised or unsupervised.
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 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 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.