I was asked a wide range of machine learning and AI-related questions, both theoretical and applied. The questions spanned topics such as: Supervised vs unsupervised learning Overfitting and underfitting Evaluation metrics like precision, recall, F1-score, and ROC-AUC Feature engineering and data preprocessing techniques Model selection and hyperparameter tuning Use cases for algorithms like Random Forest, XGBoost, and SVM Basics of deep learning and neural networks I was also asked to solve some practical ML problems live, which involved writing or explaining code, interpreting results, and debugging logic in a collaborative screen-sharing setup. These tasks tested my problem-solving skills, coding proficiency (especially in Python), and ability to think critically under time pressure.
