ML Algorithm Directory
The Complete Directory of 100+ Machine Learning Algorithms
Welcome to the most comprehensive ML algorithm reference on the web. This directory catalogs 100+ machine learning algorithms across 10 major categories, each with descriptions, use cases, key parameters, and Python code examples. Whether you are a beginner exploring ML or a practitioner choosing the right algorithm, this directory is your go-to reference.
What You Will Find
Each algorithm entry includes:
- Description — What the algorithm does and how it works
- Use Cases — When and where to apply it
- Key Parameters — The most important hyperparameters to tune
- Python Code — Ready-to-run code snippets using scikit-learn, TensorFlow, or PyTorch
- Complexity — Time and space complexity notes
Browse by Category
1. Overview
The ML algorithm landscape, selection guide, complexity comparison, and history timeline.
Start Here →2. Regression (15 Algorithms)
Linear, Polynomial, Ridge, Lasso, Elastic Net, Bayesian, SVR, Decision Tree, Random Forest, Gradient Boosting, XGBoost, LightGBM, CatBoost, Quantile, Poisson.
View Regression →3. Classification (17 Algorithms)
Logistic Regression, KNN, SVM, Decision Tree, Random Forest, Naive Bayes variants, Gradient Boosting, AdaBoost, XGBoost, LightGBM, CatBoost, SGD, Perceptron, Passive Aggressive.
View Classification →4. Clustering (11 Algorithms)
K-Means, Mini-Batch K-Means, Hierarchical, Agglomerative, DBSCAN, OPTICS, Mean Shift, Spectral, GMM, BIRCH, Affinity Propagation.
View Clustering →5. Dimensionality Reduction (10 Algorithms)
PCA, Kernel PCA, LDA, t-SNE, UMAP, ICA, Factor Analysis, NMF, Isomap, LLE.
View Dimensionality →6. Ensemble Methods (7 Algorithms)
Bagging, Boosting, Random Forest, Gradient Boosting, AdaBoost, Stacking, Voting Classifier.
View Ensemble →7. Reinforcement Learning (14 Algorithms)
Q-Learning, SARSA, DQN, Double DQN, Dueling DQN, Policy Gradient, REINFORCE, Actor-Critic, A3C, PPO, TRPO, DDPG, TD3, SAC.
View RL →8. Neural Networks & Deep Learning (14 Algorithms)
ANN, Feedforward, MLP, CNN, RNN, LSTM, GRU, Transformer, GNN, GCN, GAT, Autoencoder, VAE, GAN.
View Neural Nets →9. Time Series & Recommendation (9 Algorithms)
ARIMA, SARIMA, Prophet, Holt-Winters, State Space Models, Collaborative Filtering, Content-Based, Matrix Factorization, Factorization Machines.
View Time Series →10. Other Algorithms (10+ Algorithms)
HMM, CRF, Isolation Forest, LOF, One-Class SVM, SOM, RBM, K-Medoids, Apriori, FP-Growth, plus the master comparison table.
View Other →Quick Stats
| Total Algorithms | 107+ |
|---|---|
| Categories | 10 |
| Code Examples | Python (scikit-learn, TensorFlow, PyTorch) |
| Difficulty | All levels — Beginner to Advanced |
| Prerequisites | Basic Python, introductory statistics |