As a Machine Learning Engineer, slap yourself if you cannot clearly explain at least 10 of the following: Bias-variance tradeoff Overfitting vs underfitting Train/validation/test split strategy Cross-validation pitfalls Data leakage Feature scaling and normalization One-hot encoding vs ordinal encoding Target encoding leakage Missing value imputation strategies Outlier handling Class imbalance techniques Precision vs recall vs F1-score ROC-AUC vs PR-AUC Confusion matrix interpretation Calibration of probabilities Logistic regression internals Linear regression assumptions Ridge vs Lasso regularization ElasticNet trade-offs Gradient descent vs stochastic gradient descent Learning rate scheduling Batch size effects Loss functions: MSE, MAE, cross-entropy Convex vs non-convex optimization Vanishing and exploding gradients Backpropagation internals Activation functions: ReLU, GELU, sigmoid, tanh Batch normalization vs layer normalization Dropout regularization Weight initialization strategies CNNs and convolution internals RNNs, LSTMs, and GRUs Attention mechanism Transformers architecture Positional encoding Self-attention vs cross-attention Embedding spaces Tokenization: BPE, WordPiece, SentencePiece Fine-tuning vs feature extraction Transfer learning Prompt engineering basics LoRA and parameter-efficient fine-tuning RLHF basics Retrieval-Augmented Generation Vector databases and similarity search Cosine similarity vs dot product ANN search: HNSW, IVF, PQ Hallucination causes in LLMs Model quantization Knowledge distillation Pruning neural networks Hyperparameter tuning Grid search vs random search vs Bayesian optimization Early stopping Ensemble methods Bagging vs boosting Random Forest internals XGBoost / LightGBM / CatBoost trade-offs SHAP and feature importance Permutation importance Model interpretability vs explainability PCA and dimensionality reduction t-SNE vs UMAP K-means clustering DBSCAN clustering Anomaly detection Recommendation systems: collaborative vs content-based filtering Matrix factorization Cold-start problem Time-series forecasting basics ARIMA vs Prophet vs deep learning models Stationarity in time series Data drift vs concept drift Model monitoring Model retraining strategies A/B testing ML models Offline metrics vs online metrics MLOps pipelines Feature stores Model registries Experiment tracking MLflow basics Dockerizing ML models Batch inference vs real-time inference Shadow deployment Canary deployment for ML models Model latency optimization GPU vs CPU inference Distributed training basics Data parallelism vs model parallelism Reproducibility with random seeds Ethical ML and fairness metrics Adversarial examples Privacy-preserving ML Federated learning basics And if you only know 10, kindly return the “Senior Machine Learning Engineer” title. 😄