đš 1. Conceptual Questions (BeginnerâIntermediate) â Supervised vs. Unsupervised learning What is the difference between supervised and unsupervised learning? Give examples of real-world problems for each. â Model Understanding What is overfitting and underfitting? How do you prevent overfitting? What is the bias-variance trade-off? What are precision, recall, F1-score, and when do you prefer one over another? â Algorithms How does a decision tree work? What is the difference between logistic regression and linear regression? How does K-nearest neighbors (KNN) work? What is regularization (L1 vs. L2)? đš 2. Intermediate to Advanced Topics â Ensemble Methods How does random forest work? What is gradient boosting (e.g., XGBoost, LightGBM)? Difference between bagging and boosting? â Neural Networks What is backpropagation? What are activation functions and why are they important? Difference between CNNs and RNNs. What is dropout, and why is it used? â Optimization What are common optimizers in deep learning? How does stochastic gradient descent (SGD) differ from batch gradient descent?
