Detailed explanation
Machine learning covers supervised learning (with labels), unsupervised learning (without labels) and reinforcement learning (rewards). It powers most current AI products: recommendation, scoring, anomaly detection, classification.
Examples
Spam detection in Gmail
Credit scoring
Demand forecasting
Image recognition
Frequently asked questions
How is it different from deep learning?
Deep learning is a type of machine learning built on deep neural networks.
Related terms
Supervised Learning
Learning from labeled examples (input → expected output).
Deep Learning
A subfield of machine learning using deep neural networks to learn complex representations.
Dataset
A structured collection of data used to train, validate or evaluate an AI model.
Reinforcement Learning
A learning method where an agent learns to act by trial and error, maximizing a reward.
Unsupervised Learning
Learning from unlabeled data, where the model discovers structures by itself.
Last updated: 7/15/2026