Traditional Machine Learning
Traditional Machine Learning refers to a set of statistical and algorithmic techniques for building predictive models from data, typically without deep neural networks. It includes supervised learning (e.g., classification, regression), unsupervised learning (e.g., clustering, dimensionality reduction), and semi-supervised learning. These methods often rely on feature engineering and simpler models like decision trees, support vector machines, and linear models to extract patterns and make predictions.
Developers should learn Traditional Machine Learning for tasks where data is structured, interpretability is crucial, or computational resources are limited, such as in fraud detection, customer segmentation, or recommendation systems. It provides a solid foundation for understanding core ML concepts before diving into deep learning, and is widely used in industries like finance, healthcare, and marketing for its efficiency and transparency.