What is machine learning?
Machine learning is a field of methods that enable computer systems to learn patterns from examples and produce predictions or decisions. Instead of writing a separate rule for every situation, model parameters are adjusted using training data. Learning does not imply human-like understanding or correctness in every situation.
Training, validation and inference
Supervised learning uses examples with target answers, such as classifying email as spam. Unsupervised methods can seek groups or structures in unlabeled data. Reinforcement learning focuses on developing a behavior strategy through actions and rewards. Models need validation and test sets separate from training data. Accidental overlap between training and testing can exaggerate performance. Memorizing training examples while failing on new examples is called overfitting. Inference is the stage in which a trained model produces results for new input; it differs from training and may have different resource needs.
Data quality and interpreting results
Model performance depends on more than the algorithm. Missing, incorrect or unrepresentative data can create systematic errors. A model detecting rare equipment faults might achieve high overall accuracy by declaring most records normal while missing actual faults. Metrics must therefore reflect the intended use. User behavior or data distributions can change after training, so performance needs monitoring and reassessment. Model output should not be treated as a causal explanation or an unquestionable fact. High-impact decisions require consideration of error costs, human oversight and ways to challenge outcomes. Good practice includes ongoing evaluation of data and results, not merely building a model.