Machine Learning Tasks
Machine learning tasks refer to the different types of problems that can be solved using machine learning algorithms. These tasks can be broadly categorized into three main types: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a model on labeled data, where the algorithm learns to map input data to output labels. Unsupervised learning, on the other hand, involves training a model on unlabeled data, where the algorithm learns to identify patterns and relationships in the data. Reinforcement learning involves training a model to make decisions based on feedback from the environment, where the algorithm learns to maximize a reward signal. Other machine learning tasks include semi-supervised learning, where the model is trained on a combination of labeled and unlabeled data, and transfer learning, where a model trained on one task is used to solve a different but related task.
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