Machine Learning (ML) is a subset of artificial intelligence (AI) that involves the development and application of algorithms that allow computers to learn from and make decisions or predictions based on data. It focuses on the design of systems that can learn from and adapt to their environment, enabling machines to act intelligently without being explicitly programmed to perform a specific task.
There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.
- Supervised Learning: In supervised learning, the model is trained on a labeled dataset, i.e., a dataset where the desired output is known. The goal is to learn a mapping from inputs to outputs and make predictions on unseen data. Common tasks include regression (predicting a continuous output) and classification (predicting a categorical output).
- Unsupervised Learning: In unsupervised learning, the model is given an unlabeled dataset and must find structure in the data on its own. This could involve discovering groups or clusters in the data (clustering), finding the distribution of data (density estimation), or reducing the dimensionality of data (dimensionality reduction).
- Reinforcement Learning: In reinforcement learning, an agent learns to make decisions by performing actions in an environment to maximize some notion of cumulative reward. It differs from supervised learning in that correct input/output pairs are never presented, and sub-optimal actions are not explicitly corrected.
Machine learning is widely used across many fields including healthcare, finance, marketing, and transportation. Applications range from recommendation systems, image recognition, speech recognition, autonomous vehicles, and playing board and video games.
As machine learning algorithms continue to evolve and improve, the breadth and depth of their application continue to grow, marking machine learning as a key technology driving the AI revolution.