Machine learning
Machine learning (ML) is a branch of artificial intelligence focused on creating models that can learn from data (often large datasets) and make predictions or decisions without being explicitly programmed. Instead of following fixed rules, ML models analyze examples—historical data—to recognize patterns and improve their performance over time.
There are three main types of learning in ML.
- Supervised learning uses labeled data to predict outcomes, like identifying spam emails.
- Unsupervised learning finds patterns in unlabeled data, which is useful for segmentation or spotting anomalies.
- Reinforcement learning teaches an agent to make decisions through trial and error, often used in robotics or game-playing AI. Modern LLMs, such as ChatGPT, also use reinforcement learning to fine-tune responses and align them with human preferences after the initial supervised training.
Machine learning is now widely used across industries. It powers personalized feeds on social media and eCommerce platforms, forecasts demand and pricing, improves diagnostics in healthcare, and more.
Machine learning is also the foundation for many modern technologies, including generative AI models, AI agents, and multi-agent systems. It’s widely used in areas such as recommendation engines, fraud detection, natural language processing, sentiment analysis, and image recognition.
