Machine Learning Algorithms

Machine learning algorithms are the backbone of modern AI systems. They allow computers to learn from data and improve their performance over time. Below are some commonly used machine learning algorithms:

Supervised Learning Algorithms

Supervised learning algorithms are trained on labeled data. The goal is to learn a mapping from input variables (X) to an output variable (Y).

Linear Regression: Used for predicting continuous values.
Logistic Regression: Used for binary classification problems.
Support Vector Machines (SVM): Effective in high-dimensional spaces.
Decision Trees: Simple to understand and interpret.
Random Forest: An ensemble method that combines multiple decision trees.

Unsupervised Learning Algorithms

Unsupervised learning algorithms are trained on unlabeled data. The goal is to find patterns and relationships in the data.

K-Means Clustering: Groups data into K clusters.
Hierarchical Clustering: Creates a hierarchy of clusters.
Principal Component Analysis (PCA): Reduces dimensionality of the data.
Association Rules: Finds relationships between variables.

Reinforcement Learning Algorithms

Reinforcement learning algorithms learn by interacting with an environment and receiving rewards or penalties.

Q-Learning: A model-free method that uses a Q-table to learn the optimal action for each state.
Policy Gradient Methods: Learn a policy that maps states to actions.
Deep Q-Network (DQN): Combines Q-learning with deep neural networks.

Resources

For more information on machine learning algorithms, you can visit our Machine Learning Basics page.