Machine Learning Models
Machine learning models are algorithms that can learn from data and make predictions or decisions without being explicitly programmed. These models use statistical techniques to identify patterns in data and use those patterns to make predictions or decisions. Machine learning models can be supervised, unsupervised, or semi-supervised, depending on the type of data available and the problem being solved. Supervised learning models are trained on labeled data, where the correct output is known, and are used for tasks such as classification and regression. Unsupervised learning models are trained on unlabeled data and are used for tasks such as clustering and anomaly detection. Semi-supervised learning models are trained on a combination of labeled and unlabeled data. Machine learning models can be further categorized into different types such as decision trees, random forests, neural networks, support vector machines, and many others.
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