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What Is AI Learning? A Comprehensive Introduction
Machine Learning Foundations and Lifecycle
Machine learning is an AI subfield that builds models to learn patterns from data, covering its paradigms, lifecycle, mathematics, and common algorithms.
- Supervised, unsupervised, and reinforcement learning describe the three main paradigms.
- Standard dataset partitioning allocates 70 % for training, 15 % for validation, and 15 % for testing.
- The ML lifecycle progresses through problem definition, data collection/preprocessing, feature engineering, model training, evaluation/tuning, and deployment/monitoring, with data quality and overfitting as key concerns.
- Understanding linear algebra, calculus (gradient descent), and probability/statistics is essential for model development.
- Typical algorithms include linear regression, decision trees, k‑means clustering, and neural networks.