List of Machine Learning Models
2 min readAug 16, 2023
Comprehensive list of various types of machine learning models:
- Linear Regression
- Ridge Regression
- Lasso Regression
- Elastic Net Regression
- Logistic Regression
- Decision Trees
- Random Forest
- Gradient Boosting Machines (GBM)
- XGBoost
- LightGBM
- CatBoost
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
- Principal Component Analysis (PCA)
- Independent Component Analysis (ICA)
- Non-Negative Matrix Factorization (NMF)
- Gaussian Mixture Models (GMM)
- Hidden Markov Models (HMM)
- Neural Networks (Feedforward, Convolutional, Recurrent)
- Long Short-Term Memory (LSTM)
- Gated Recurrent Units (GRU)
- Autoencoders
- Variational Autoencoders (VAE)
- Generative Adversarial Networks (GAN)
- Deep Q-Networks (DQN)
- Actor-Critic Models
- Temporal Difference Learning
- Gaussian Process Models
- Kernel Methods
- Multilayer Perceptrons (MLP)
- Word Embeddings (Word2Vec, GloVe)
- Transformer Models (BERT, GPT, T5)
- Sequence-to-Sequence Models
- Hierarchical Models
- Dynamic Time Warping (DTW)
- Hierarchical Clustering
- Mean Shift Clustering
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- Agglomerative Clustering
- Self-Organizing Maps (SOM)
- Isolation Forest
- One-Class SVM
- Anomaly Detection Models
- Time Series Models (ARIMA, SARIMA, Exponential Smoothing)
- Hidden Markov Models (HMM) for Time Series
- Gaussian Process Regression
- Bayesian Networks
- Association Rule Learning (Apriori, FP-Growth)
- Markov Chains
- Reinforcement Learning Models (Q-Learning, SARSA, Policy Gradient)
Each of these models has its own characteristics, advantages, and limitations. The choice of model depends on the nature of the data, the problem you’re trying to solve, and your specific goals.
