Feature engineering is the process of using domain knowledge to extract features from raw data that make machine learning algorithms work effectively. It involves selecting, modifying, or creating new features to improve model performance. Common techniques include normalization, encoding categorical variables, and generating interaction terms. Feature engineering is crucial in various applications such as predictive modeling, image recognition, and natural language processing, as the quality of features directly impacts the accuracy of the model.
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AI Fundamentals