ii. Machine Learning & AI Concepts

Machine learning and AI concepts describe computational methods that enable systems to learn from data, identify patterns, and make predictions or recommendations. These techniques extend traditional analytics by adapting to new information without explicit reprogramming.

Artificial Intelligence (AI)

A broad field of computer science focused on systems that perform tasks typically requiring human judgment.

Feature

An individual input variable used by a model to make predictions or classifications.

Inference

The process by which a trained model generates outputs from new data.

Machine Learning (ML)

A subset of AI that uses data-driven algorithms to improve performance over time.

Model

A mathematical or statistical representation used to map inputs to outputs.

Model Training

The process of fitting a model to historical data so it can learn patterns.

Supervised Learning

A training approach using labeled data to learn relationships between inputs and known outcomes.

Unsupervised Learning

A training approach that identifies patterns or groupings in unlabeled data.

Validation Dataset

A subset of data used to evaluate model performance during training.

Overfitting

A condition where a model learns noise rather than generalizable patterns, reducing real-world accuracy.