Which of the following best describes how machine learning's trial$-$and$-$error learning process works?

Machine learning adjusts itself by constantly testing new data against its corpus, making modifications based on established patterns.

Machine learning learns by memorizing all possible outcomes and selecting the most common one as the correct answer.

Machine learning makes random adjustments to its algorithms without testing them against existing data.

Machine learning relies solely on preset algorithms to generate results without any adjustment or modification.