Types of Machine Learning Algorithm

  1. Linear Regression
  2. Logistic Regression
  3. Decision Trees
  4. Random Forest
  5. Support Vector Machines (SVM)
  6. K-Nearest Neighbors (KNN)
  7. Naive Bayes
  8. Gradient Boosting (e.g., XGBoost, LightGBM)
  9. Neural Networks (Deep Learning)

  1. K-Means Clustering
  2. Hierarchical Clustering
  3. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
  4. Gaussian Mixture Models (GMM)
  5. Principal Component Analysis (PCA)
  6. Independent Component Analysis (ICA)
  7. Autoencoders (for dimensionality reduction)
  8. t-Distributed Stochastic Neighbor Embedding (t-SNE)

  1. Self-training
  2. Multi-view learning
  3. Co-training

  1. Q-Learning
  2. Deep Q Network (DQN)
  3. Policy Gradient Methods (e.g., REINFORCE)
  4. Actor-Critic

  1. Bagging (e.g., Bootstrap Aggregating)
  2. Boosting (e.g., AdaBoost, Gradient Boosting Machines)
  3. Stacking

  1. Isolation Forest (Anomaly Detection)
  2. One-Class SVM (Anomaly Detection)
  3. ARIMA (Time Series Forecasting)
  4. XGBoost (Extreme Gradient Boosting)
  5. LightGBM (Light Gradient Boosting Machine)
  6. Word Embeddings (e.g., Word2Vec, GloVe) for Natural Language Processing
  7. Recurrent Neural Networks (RNN) for Sequential Data
  8. Long Short-Term Memory (LSTM) Networks
  9. Transformer Models (e.g., BERT) for Natural Language Processing