ML approaches and use cases
AI/ML applications are usually intended to make some kind of prediction based on input data, with perhaps the exception of Generative AI, because Generative AI is intended to generate content rather than simply making predictions. In order to make predictions, ML models first need to be trained, and how they are trained depends on the approach being used. While ML is a broad concept that encompasses many different fields of research, with endless new use cases being created almost every day, the industry generally groups ML approaches into three high-level categories:
- Supervised learning (SL)
- Unsupervised learning (UL)
- Reinforcement learning (RL)
SL
SL is the most commonly used type of ML in the industry and perhaps the easiest to describe. The term supervised indicates that we are informing the ML model of the correct answers during the training process. For example, let’s imagine that we want to train a model to be able...
