Classical Machine Learning
Classical machine learning refers to traditional, non-deep learning algorithms that learn patterns from data to make predictions or decisions without being explicitly programmed. It encompasses supervised learning (e.g., regression, classification), unsupervised learning (e.g., clustering, dimensionality reduction), and reinforcement learning, often relying on statistical methods and feature engineering. These algorithms are widely used for tasks like spam detection, recommendation systems, and customer segmentation.
Developers should learn classical machine learning for interpretable, efficient solutions in scenarios with limited data, where deep learning might be overkill or computationally expensive. It's essential for foundational understanding before diving into deep learning, and it excels in structured data problems like credit scoring, fraud detection, and predictive maintenance in industries like finance and healthcare.