Training algorithms are methods used in machine learning to help models learn from data. They adjust the model's parameters based on the input data and the desired output, aiming to minimize errors. Common training algorithms include gradient descent and stochastic gradient descent, which iteratively update the model to improve its predictions. These algorithms work by processing a dataset, which consists of features and labels. The model makes predictions, and the training algorithm evaluates the accuracy of these predictions. By continuously refining the model through multiple iterations, it becomes better at recognizing patterns and making accurate predictions on new data.