Machine learning: is essentially statistics wrapped in learning software that adjusts the parameters of the model to improve its accuracy.
- Supervised learning: necessitates labeling the data so that the software may learn whether it is correct or incorrect. This is used for identifying and forecasting things. Teaching automobiles to recognize objects on the road using deep neural networks and learning the warning signs for a jet engine predictive maintenance system are two examples of this.
- Unsupervised learning: requires just raw data. It is used in recommender systems, such as those used by Netflix and Amazon, to group or associate items together.
- Reinforcement learning: necessitates the use of data and rules. It is employed in player AI in EA Sports games as well as in the Roomba vacuum robot.