Standard Machine Learning
Standard Machine Learning refers to the foundational and widely-used techniques in machine learning that involve training models on labeled or unlabeled data to make predictions or discover patterns, typically excluding deep learning. It encompasses supervised learning (e.g., regression, classification), unsupervised learning (e.g., clustering, dimensionality reduction), and semi-supervised learning, often implemented with algorithms like linear regression, decision trees, and k-means. This approach focuses on traditional statistical and computational methods for data analysis and automation.
Developers should learn standard machine learning to build predictive models for tasks such as customer segmentation, fraud detection, and recommendation systems, where interpretability and efficiency are prioritized over complex neural networks. It is essential for applications in finance, healthcare, and marketing that rely on structured data and require model transparency, making it a core skill for data scientists and engineers working on real-world, scalable solutions.