← All categoriesData & Feature Engineering
Where most of your model quality comes from, and where interviewers dig for rigour.
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24🟢Feature Engineering Fundamentalsmust-know4 min🟡SQL Questions in ML Interviewsmust-know5 min🟢Encoding Categorical Variables4 min🟢Scaling & Normalization5 min🟢Datetime & Cyclical Features5 min🟢Turning Text into Features5 min🟡High-Cardinality Categoricals5 min🟡Feature Crosses & Interactions5 min🟡Feature Selection Methods5 min🟡Using Embeddings as Features5 min🟡Data Quality & Validation5 min🟡Labelling: Weak Supervision & Annotation5 min🟡Inter-Annotator Agreement5 min🟡Sampling from Huge Datasets5 min🟡Deduplication & Near-Duplicate Detection5 min🟡SQL Window Functions5 min🟡Streaming vs Batch Data5 min🟡Lake vs Warehouse vs Lakehouse5 min🟡Parquet & Columnar Storage5 min🟡Handling PII in Training Data5 min🔴Target Encoding Without Leakage5 min🔴Schema Evolution & Contracts5 min🔴Spark & Distributed Data Processing5 min🔴Data Skew & Shuffle Costs5 min