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Data Preprocessing

Cleaning, transforming, and preparing raw data for model training (handling missing values, normalization, encoding).

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  1. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

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Data Pipeline Image Preprocessing

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Foundations Data Cleaning Finding and fixing errors, inconsistencies and gaps in a dataset (duplicates, typos, missing values, wrong labels) before a model learns from it. Foundations Standardization Rescaling each feature to mean 0 and standard deviation 1, so features measured in different units are on a comparable scale. 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).