Traditional Machine Learning Models
Traditional machine learning models are statistical and algorithmic approaches for pattern recognition and prediction, developed before the rise of deep learning. They include supervised, unsupervised, and semi-supervised techniques such as linear regression, decision trees, and clustering algorithms. These models are often interpretable, computationally efficient, and effective for structured or tabular data with clear feature representations.
Developers should learn traditional ML models for tasks involving structured data, such as customer segmentation, fraud detection, or sales forecasting, where interpretability and efficiency are critical. They are particularly useful when data is limited, computational resources are constrained, or regulatory requirements demand transparent decision-making, as in finance or healthcare applications.