A: There are three key areas of machine learning.
Supervised learning is a type of data science that uses labeled data that’s been tagged with specific information about outcomes associated with that data. A model is then trained to learn what features or variables are predicting the outcomes assigned to the labeled input data. The model can then use the information from the output data to assess its own performance and predict outcomes.
Supervised learning involves two main use cases: classification and linear regression. Classification predicts a class label — for example, you might predict whether a customer will cut ties with a brand based on attributes such as purchase behavior.
Linear regression predicts a numerical label, like the expected revenue you think you’ll receive from a customer based on specific attributes. The outcome is some kind of numerical variable, as opposed to a condition.
Unsupervised learning starts with a set of raw, unlabeled data. The main purpose of unsupervised learning is to find connections between the data set and any additional data points you give the model. This method can help you find relationship-based groups within data, or clusters, which can be used to create customer segments.
Reinforcement learning starts by inputting a set of raw, unlabeled data into a model. The model then takes some kind of action. Based on that action, the model receives feedback on whether it acted correctly or incorrectly and the outcomes of that action. The model then creates another action and keeps learning until model optimization is achieved.
A great real-world example of reinforcement learning is a recommendation algorithm on a movie streaming service like Netflix. The service shows you a movie you may or not like and learns from your “like” or “dislike” rating whether it should keep recommending the same types of movies.