At bridge_ci we use all types of machine-learning techniques

Supervised learning is a type of machine learning in which the algorithm is trained on a labeled dataset, meaning that the correct output is provided for each example in the training set. The algorithm uses the labeled training data to learn the relationship between the input and the output, and can then make predictions on new, unseen data. Supervised learning is often used for tasks such as classification, regression, and prediction.

Semi-supervised learning is a type of machine learning that is intermediate between supervised and unsupervised learning. In semi-supervised learning, the algorithm is trained on a dataset that is partially labeled, with only some of the examples in the training set having known output values. The algorithm can use the labeled examples to learn the relationship between the input and the output, and can then use the unlabeled examples to make predictions or to refine its understanding of the data. Semi-supervised learning is often used when labeled data is scarce, or when the cost of labeling data is high.

Unsupervised learning is a type of machine learning in which the algorithm is not provided with labeled training examples. Instead, the algorithm is trained on an unlabeled dataset and must discover the underlying structure and relationships in the data on its own. Unsupervised learning is often used for tasks such as clustering, dimensionality reduction, and anomaly detection.