There are different categories of systems available for Machine Learning. These systems are mostly classified based on below criteria:
- Systems that require human supervision for example Supervised , Unsupervised , Semi supervised and Reinforcement learnings.
- Systems that can learn on the fly for example Online vs Batch learning.
- Instance based or model based learning.
Instance based system learns the examples and tries to compare new inputs with the learned examples. For example Spam Filter, system learns the spam email and then flags the incoming email by comparing with the learned Spam emails.Model based systems creates model and from set of example and then uses the model to make predictions. For example Housing Price prediction system can create model based on set of features and then make prediction for new Houses by using the model.
- Hybrid System that leverages combination of above approach. For example Spam Filter can use on the fly learning approach using neural network models making it an on online , model based and supervised learning system.
