Training data is the initial dataset used to train a machine learning model to recognize patterns or perform a desired task. It is an extremely large dataset that is used to teach a machine learning model. The quality of the training data is crucial to the performance of the algorithm.

Training data is fed to machine learning algorithms to teach them how to make predictions or perform a desired task. The algorithm develops confidence in its decisions by understanding the underlying patterns, relationships, and structures within a training dataset.

Training data can be labeled or unlabeled, depending on whether supervised or unsupervised learning is used. In supervised learning, the training data is enriched with data labeling or annotation.

Test data is used to measure the performance, such as accuracy or efficiency, of the algorithm used to train the machine. The difference between training data and test data is that training data is used to teach the machine learning model, while test data is used to evaluate the models accuracy.

In summary, training data is the initial dataset used to teach a machine learning model to recognize patterns or perform a desired task. It is crucial to the performance of the algorithm, and can be labeled or unlabeled depending on the type of learning used. Test data is used to evaluate the accuracy of the model.