Machine learning gives organizations the potential to make more accurate data-driven decisions.
Classification
Based on a set of training data, categorize new inputs as belonging to one of a set of categories. An example of classification is identifying whether an image contains a specific type of object, such as a cat or a dog, or a product of acceptable quality coming from a manufacturing line.
Anomaly detection
Given a training set of data, determine whether specific inputs are out of the ordinary. For instance, a system could be trained on a set of historical vibration data associated with the performance of an operating piece of machinery, and then determine whether a new vibration reading suggests that the machine is not operating normally. Anomaly detection can be considered a subcategory of classification.
Continuous estimation
Based on a set of training data, estimate the next numeric value in a sequence. This type of problem is sometimes described as “prediction,” particularly when it is applied to time series data. One example of continuous estimation is forecasting the sales demand for a product, based on a set of input data such as previous sales figures, consumer sentiment, and weather.
Recommendations
These systems provide recommendations based on a set of training data. A common example of recommendations are systems that suggest “next product to buy” for an individual buyer, based on the buying patterns of similar individuals, and the observed behavior of the specific person.
Clustering
These problems require a system to create a set of categories, for which individual data instances have a set of common or similar characteristics. An example of clustering is creating a set of consumer segments, based on a set of data about individual consumers, including demographics, preferences, and buyer behavior.
Data generation
These problems require a system to generate appropriately novel data based on training data. For instance, a music composition system might be used to generate new pieces of music in a particular style, after having been trained on pieces of music in that style.