The four basics of machine learning are:
Data: Machine learning requires a dataset that is representative of the problem or task at hand.
Algorithms are mathematical models and techniques that process the data and make predictions or decisions.
Training: The algorithm is trained on the dataset to learn patterns and relationships.
Evaluation: The trained model is evaluated on new data to assess its performance and accuracy.