Machine learning is a branch of AI that allows computers to learn without having to be explicitly programmed. Machine learning algorithms can learn from their mistakes and improve over time, becoming more accurate and efficient in predicting outcomes. There are a number of distinct types of machine learning algorithms, but supervised and unsupervised learning are the most prevalent. The method is trained with a set of data that includes the intended outputs, allowing it to learn how to predict the outputs for fresh data. Unsupervised learning algorithms are given data to study rather than desired outputs so that they can learn to group and categorize data on their own. Testing is the process of confirming the correctness of a machine learning algorithm's predictions on a set of data that was not used to train the system. The process of teaching a machine-learning algorithm to make predictions by altering its parameters to maximize its performance on a set of training data is referred to as training.