Setting up the goal for a machine learning project involves establishing well-defined evaluation metrics and datasets based on key principles:

1. Single-number evaluation metric: Select a consistent metric for comparing different deep learning models.
2. Satisficing and optimizing metrics: You can have multiple satisficing metrics (thresholds to meet) but only one optimizing metric.
3. Dev and test set distribution: Both datasets must have the same distribution. Choose dev and test sets that reflect the data you expect to get in the future and consider important to do well on.
4. Dev and test set sizes: For large datasets (e.g., containing 1,000,000 samples), the training dataset can have 98% of the data, while the dev and test sets only have 1% each. This splitting method differs from traditional machine learning algorithms.