ML Algorithm Library
Explore our comprehensive collection of machine learning algorithms, from classical methods to cutting-edge deep learning architectures.
Linear Regression
Predict continuous values by fitting a linear relationship between input features and target variable.
Price prediction, trend analysis
Random Forest
Ensemble of decision trees that reduces overfitting through bagging and feature randomization.
Classification, feature importance
Support Vector Machine
Find optimal hyperplane to separate classes with maximum margin in high-dimensional space.
Text classification, image recognition
K-Means Clustering
Partition data into K clusters by minimizing within-cluster variance iteratively.
Customer segmentation, anomaly detection
Principal Component Analysis
Reduce dimensionality while preserving variance through orthogonal transformation.
Feature reduction, visualization
DBSCAN
Density-based clustering that identifies arbitrarily shaped clusters and outliers.
Spatial data, noise filtering
Q-Learning
Model-free algorithm learning optimal action-value function through temporal difference.
Game AI, robotics control
Policy Gradient
Directly optimize policy parameters using gradient ascent on expected reward.
Continuous control, NLP generation
Convolutional Neural Network
Specialized architecture for grid-like data using convolutional filters and pooling.
Image classification, object detection
Transformer
Attention-based architecture enabling parallel processing of sequential data.
Language models, translation
Generative Adversarial Network
Two networks compete: generator creates samples, discriminator evaluates authenticity.
Image generation, data augmentation
Recurrent Neural Network
Process sequential data with hidden states carrying information through time steps.
Time series, speech recognition