@TensorTonic: 13 Core ML Concepts Every Interviewer Expects You to Know 1. Bias-Variance Tradeoff - The key framework for understandi…

13 Core ML Concepts Every Interviewer Expects You to Know 1. Bias-Variance Tradeoff - The key framework for understanding model performance and generalization. 2. Overfitting vs. Underfitting - How models fail and how regularization helps strike the right balance. 3. L1 vs. L2 Regularization - L1 encourages sparsity through feature selection, while L2 shrinks weights to improve generalization. 4. Cross-Validation - Especially k-fold cross-validation and why it's more reliable than a single train/test split. 5. Data Leakage - How information unintentionally leaks into training, leading to overly optimistic offline metrics. 6. Precision, Recall & F1 Score - What each metric measures and when to optimize one over the others. 7. ROC-AUC vs. PR-AUC - Why PR-AUC is often the better choice for highly imbalanced datasets. 8. Backpropagation - Applying the chain rule to efficiently compute gradients in neural networks. 9. Vanishing & Exploding Gradients - Why they happen and how techniques like ReLU, residual connections, normalization, and gradient clipping help. 10. Class Imbalance - Handling skewed datasets with resampling, class weights, focal loss, and threshold tuning. 11. Bagging vs. Boosting - Bagging primarily reduces variance, while boosting primarily reduces bias. 12. Maximum Likelihood Estimation (MLE) - The statistical principle behind many commonly used machine learning loss functions. 13. Curse of Dimensionality - Why distance-based methods and model generalization become harder in high-dimensional spaces.