Machine learning algorithms are the backbone of modern AI systems. They allow computers to learn from data, make predictions, and perform tasks without being explicitly programmed. Here are some of the most common machine learning algorithms:
Supervised Learning Algorithms
Supervised learning algorithms learn from labeled training data. They are used to predict outcomes based on input features.
- Linear Regression: Used for predicting continuous values.
- Logistic Regression: Used for predicting binary outcomes.
- Support Vector Machines (SVM): Effective in high-dimensional spaces.
- Decision Trees: Non-parametric algorithm that can handle both categorical and numerical data.
- Random Forest: An ensemble learning method that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.
Unsupervised Learning Algorithms
Unsupervised learning algorithms learn from unlabeled data. They are used to discover hidden patterns or groupings in the data.
- K-Means Clustering: A method of vector quantization that is popular for cluster analysis in data mining.
- Hierarchical Clustering: A method of cluster analysis which is hierarchical in nature.
- Principal Component Analysis (PCA): A dimensionality-reduction method that is often used to reduce the dimensionality of large datasets.
- Association Rules: Used for learning simple conditional statements about the database records.
Reinforcement Learning Algorithms
Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment to achieve a goal.
- Q-Learning: A value-based method that learns the optimal action-value function.
- Policy Gradient: A family of algorithms that learn to map states to actions.
- Deep Q-Network (DQN): A deep learning algorithm that combines Q-learning with a deep neural network.
For more information on machine learning algorithms, you can visit our Machine Learning Basics guide.