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Data Preprocessing
Cleaning, transforming, and preparing raw data for model training (handling missing values, normalization, encoding).
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A collection of data examples used for training, validating, or testing machine learning models.
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Foundations Normalization Scaling features to a standard range (typically 0-1 using min-max scaling) to improve model training and convergence. Often used interchangeably with standardization (mean=0, std=1), though technically distinct. Foundations One-Hot Encoding Converting categorical variables into binary vectors with one element set to 1 and others to 0. Foundations Feature Engineering The process of selecting, transforming, and creating input features to improve model performance. Training Data Augmentation Creating variations of training data through transformations (rotation, cropping, noise) to improve model generalization. Vision & Multimodal Image Preprocessing Transforming images before model input (resizing, normalization, color adjustment).