What are the different types of machine learning?
Machine learning can be broadly categorized into three main types:
- Supervised Learning: In supervised learning, the algorithm learns from labeled data, meaning the input data is accompanied by the correct output. The algorithm learns to map the input to the output based on this labeled data. Common supervised learning tasks include classification (predicting labels/categories) and regression (predicting continuous values). Examples include linear regression, logistic regression, decision trees, support vector machines (SVM), and neural networks.
- Unsupervised Learning: Unsupervised learning deals with unlabeled data, where the algorithm learns to identify patterns or structures within the data without explicit guidance. The goal is to explore the data and find meaningful insights such as clusters, associations, or anomalies. Common unsupervised learning techniques include clustering algorithms (like k-means clustering, hierarchical clustering) and dimensionality reduction techniques (such as principal component analysis or PCA, and t-distributed stochastic neighbor embedding or t-SNE).
- Reinforcement Learning: Reinforcement learning involves training an agent to make decisions within an environment in order to maximize some notion of cumulative reward. It operates on the principle of trial and error, where the agent learns to achieve a goal (maximize reward) through repeated interactions with the environment. The agent learns by receiving feedback in the form of rewards or penalties as it navigates through its problem space. Reinforcement learning has applications in various domains, including game playing, robotics, recommendation systems, and autonomous vehicle control.
These are the main categories, but there are also some specialized areas and hybrid approaches such as semi-supervised learning, self-supervised learning, and transfer learning, which combine elements of the three main types or extend them in various ways to tackle specific challenges or tasks.