Machine learning algorithms are the backbone of artificial intelligence. They enable computers to learn from data and make decisions or predictions based on that data. Below is a list of some common machine learning algorithms:
Supervised Learning Algorithms
- Linear Regression: Used for predicting a continuous outcome.
- Logistic Regression: Used for predicting a binary outcome.
- Support Vector Machines (SVM): A powerful classifier that can be used for both classification and regression tasks.
- Decision Trees: A non-parametric supervised learning algorithm that can be used for both classification and regression.
Unsupervised Learning Algorithms
- K-Means Clustering: A clustering algorithm that divides the data into K clusters.
- Principal Component Analysis (PCA): A dimensionality reduction technique that transforms the data into a lower-dimensional space.
- Association Rules: Used for discovering interesting relations between variables in large databases.
Reinforcement Learning Algorithms
- Q-Learning: A value-based method that learns the optimal policy by estimating the value function.
- Policy Gradient: A policy-based method that learns the optimal policy by directly optimizing the expected return.
For more information on machine learning algorithms, you can visit our Machine Learning Basics page.