Key components of machine learning:-
Data input: large,diverse datasets are required for training.
Algorithms: Mathematical methods that identify patterns.
Training: The process of optimiszing a model to identify patterns in data.
Generalization: The ability of a trained model to make accurate predictions on new, unseen Data.
Primary types of machine Learning ;
Supervised learning - Algorithms are trained on lebled data, meaning the input comes with the correct answer.
Unsupervised learning - Algorithms anlyze unlabeled data to find hidden structures or patterns.
Semi supervised learning - Uses a mix of labled and unlabeled data, typically a small amount of labled with a large amount of unlabeled.
Reinforcement lerning- Algorithms learn by interacting with an environment, receiving rewards for correct actions and penalties for wrong ones, common in robotics and gaming.
Common application -
Computer vision
Predictive Analytics
Natural language processing
Recommendation Engines