How can machine learning algorithms be trained to recognize patterns in large datasets and make predictions based on that data?

Machine learning algorithms are trained to recognize patterns in large datasets through a process called supervised learning. During training, the algorithm is fed labeled data, where input features are paired with corresponding desired outputs. The algorithm learns to map inputs to outputs by adjusting internal parameters. Once trained, it can make predictions on new, unseen data by generalizing from learned patterns. This process involves iterative optimization, often using techniques like gradient descent. Regularization methods are applied to prevent overfitting. The trained model's performance is evaluated on a separate dataset to ensure it can generalize well to real-world scenarios. i always ideas from trusted site like getting over it game apk