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Types of Machine Learning
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Reinforcement Learning
A type of learning where agents learn to make decisions by taking actions in an environment to maximize cumulative reward. Example: A chess-playing algorithm that improves by playing games and learning from wins and losses.
Semi-Supervised Learning
A mix between supervised and unsupervised learning where a model is trained on a combination of labeled and unlabeled data. Example: A photo tagging system that uses a small set of labeled photos and a large set of unlabeled photos to improve accuracy.
Unsupervised Learning
Learning from unlabeled data to find hidden patterns or intrinsic structures. Example: Customer segmentation in marketing based on purchasing behavior.
Supervised Learning
A type of machine learning where models are trained on labeled data. Example: A spam filter that classifies emails as 'spam' or 'not spam' based on labeled examples.
Transfer Learning
A method where a pre-trained model is reused as the starting point for a model on a second task. Example: Using a model trained on a large dataset of different objects to initialize a new model for recognizing specific types of birds.
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