1. Supervised Learning
Supervised Learning is a machine learing type in which model is trained using data which is already tagged with correct anwer(label). it can be further grouped into Classifacion and Regression .
2. Unsupervised Learning
Unsupervised Learning is a machine learing type in which model is trained using data that is neither classified nor labelled and allowing the model to act on that information without guidance. Clustring ,self supervised and auto encoding are all unsupervised learning methodes.
3. Reinforcement Learning
Reinforcement learning is a learning by intracting with an environment. It enables an agent to learn in an intractive envoirmnet by trail and erorr using feedback from its own actions and experiences.
4. Transfer Learning
Transfer Learning enables us to utlize the knowledge from previous learned tasks and apply them to newer, related ones. E.g knowledge gained while learning to recognize cats can be used to some extent to recongize dogs.
5. Multi-Task Learning
Multi- task learning aims to solve multiple differnet tasks at the same time, by taking advantage of the similarities between diffenent tasks, using a shared representation.
6. Ensamble Learning
Ensamble Learning combines the preidctions from multiple models to improve the overall performance. Bagging and Boosting are the most common ensemble techniques.