Comprehensive guide to data augmentation techniques for training robust deep learning models. Why Data Augmentation? Benefits: Increases training data diversity Reduces overfitting Improves model generalization Makes models robust to variations Cost-effective alternative to collecting more data Common Use Cases: …

Common patterns and workflows for scikit-learn: preprocessing, model training, evaluation, and pipelines. Installation 1pip install scikit-learn numpy pandas matplotlib Basic Workflow 1from sklearn.model_selection import train_test_split 2from sklearn.preprocessing import StandardScaler 3from sklearn.linear_model …