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.
Developers should learn Traditional Machine Learning for scenarios with limited data, interpretability requirements, or when computational resources are constrained, such as in fraud detection, recommendation systems, or customer segmentation. 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 tasks like predictive analytics and pattern recognition.