Ability to perform feature engineering tasks effectively.
Knowledge of key supervised learning problems, including classification and regression.
Understanding of the underlying mechanisms of classification algorithms (e.g., neural networks, logistic regression, decision trees, etc.).
Familiarity with model evaluation and comparison metrics.
Ability to identify underfitting and overfitting, and apply appropriate mitigation strategies.
Competence in selecting and applying classification and regression models to real-world problems, with accurate interpretation of the results.