Machine learning is a subset of artificial intelligence (AI) that involves building systems that can automatically learn from data and improve their performance over time without being explicitly programmed. In other words, instead of being explicitly programmed with a set of rules to follow, machine learning models use statistical algorithms to learn from data and make predictions or decisions.
There are three main types of machine learning:
- Supervised learning: This involves training a model on labeled data, where the input data and the desired output are both provided. The model learns to map the inputs to the correct outputs, and can then be used to make predictions on new, unseen data.
- Unsupervised learning: This involves training a model on unlabeled data, where the input data is provided but there are no corresponding output labels. The model learns to find patterns or structures in the data, and can be used for tasks such as clustering, anomaly detection, or dimensionality reduction.
- Reinforcement learning: This involves training a model to interact with an environment and learn from feedback in the form of rewards or punishments. The model learns to take actions that maximize its reward over time, and can be used for tasks such as game playing or robotics.
Machine learning has a wide range of applications, including image and speech recognition, natural language processing, recommendation systems, and predictive analytics, among others.