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All Machine Learning Algorithms To Study

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All Machine Learning Algorithms To Study

Table Of Contents:

  1. Regression Algorithms.
  2. Classification Algorithms.
  3. Clustering Algorithms.
  4. Ensemble Learning Algorithms.
  5. Dimensionality Reduction Algorithms
  6. Association Algorithms.
  7. Reinforcement Learning Algorithms.
  8. Deep Learning Algorithms.

(1) Regression Algorithms

  1. Linear Regression.
  2. Regression Trees.
  3. Non-Linear Regression.
  4. Bayesian Linear Regression.
  5. Polynomial Regression.
  6. LASSO Regression.
  7. Ridge Regression.
  8. Weighted Least Squares Regression.

(2) Classification Algorithms

  1. Logistic Regression
  2. Decision Trees
  3. Random Forest
  4. Support Vector Machines
  5. K – Nearest Neighbors
  6. Naive Bayes Algorithm

(3) Clustering Algorithms

  1. K-Means Clustering
  2. K-Medoids (PAM)
  3. Hierarchical Clustering (Agglomerative and Divisive)
  4. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
  5. Mean Shift Clustering
  6. Gaussian Mixture Models (GMM)
  7. Spectral Clustering
  8. Affinity Propagation
  9. Birch (Balanced Iterative Reducing and Clustering using Hierarchies)
  10. Self-Organizing Maps (SOM)
  11. OPTICS (Ordering Points To Identify the Clustering Structure)
  12. Fuzzy C-Means
  13. HDBSCAN (Hierarchical DBSCAN)
  14. Agglomerative Clustering

(4) Ensemble Learning Algorithms

  1. Bagging
    • Random Forests
    • Bagged Decision Trees
    • Bagging with k-Nearest Neighbors (k-NN)
    • Bagged Support Vector Machines (SVM)
    • Bagging with Neural Networks
    • Bagging with Decision Stumps
    • Bagging with Logistic Regression
  2. Boosting
    • AdaBoost
    • Gradient Boosting
    • XGBoost
    • LightGBM
    • CatBoost (Categorical Boosting)
    • Stochastic Gradient Boosting
    • LogitBoost
    • Model-based Boosting (MART)
    • BrownBoost
  3. Stacking
    • Stacked Generalization
    • Stacking with Logistic Regression as Meta-Model
    • Stacking with Linear Regression as Meta-Model
    • Stacking with Decision Trees as Meta-Model
    • Stacking with Random Forest as Meta-Model
    • Stacking with Gradient Boosting as Meta-Model
    • Stacking with Neural Networks as Meta-Model
    • Multi-Level Stacking (or Multi-Layer Stacking)
    • Stacking with Support Vector Machines (SVM) as Meta-Model
    • Stacking with K-Nearest Neighbors (k-NN) as Meta-Model
    • Weighted Stacking
    • Voting Classifier (Stacking with Majority Voting)

(4) Dimensionality Reduction Algorithms

  1. Principal Component Analysis (PCA)
  2. Independent Component Analysis (ICA)
  3. t-Distributed Stochastic Neighbor Embedding (t-SNE)
  4. Uniform Manifold Approximation and Projection (UMAP)
  5. Linear Discriminant Analysis (LDA)
  6. Singular Value Decomposition (SVD)
  7. Autoencoders
  8. Non-negative Matrix Factorization (NMF)
  9. Isomap
  10. Locally Linear Embedding (LLE)
  11. Multi-Dimensional Scaling (MDS)
  12. Principal Component Regression (PCR)
  13. Factor Analysis
  14. LLE (Locally Linear Embedding)
  15. Deep Belief Networks (DBNs)

(5) Association Algorithms

  1. Apriori Algorithm
  2. Eclat (Equivalence Class Transformation)
  3. FP-Growth (Frequent Pattern Growth)
  4. RARM (Relational Association Rule Mining)
  5. CARMA (Classification Association Rule Mining)
  6. CHARM (Closed Association Rule Mining)
  7. SPMF (Sequential Pattern Mining Framework)
  8. Partition Algorithm
  9. MAAR (Mining Association Rules in Large Datasets)
  10. H-Mine (Horizontal Mining)
  11. LCM (Linear Time Closed Itemset Mining)
  12. Constraint-based Association Rule Mining
  13. TID (Transaction ID) List-based Algorithms

(6) Reinforcement Learning Algorithms

  1. Value-Based Algorithms
    • Q-Learning
    • Deep Q-Network (DQN)
    • Double Q-Learning
    • SARSA (State-Action-Reward-State-Action)
    • Expected SARSA
  2. Policy-Based Algorithms
    • REINFORCE (Monte Carlo Policy Gradient)
    • Actor-Critic Methods
  3. Model-Based Algorithms
    • Dyna-Q
    • Monte Carlo Tree Search (MCTS)
  4. Off-Policy Algorithms
    • Q-Learning
    • Deep Q-Learning
  5. Evolutionary Algorithms
    • Genetic Algorithms
    • Neuroevolution
  6. Inverse Reinforcement Learning (IRL)
  7. Multi-Agent Reinforcement Learning (MARL)

(7) Deep Learning Algorithms

  1. Artificial Neural Networks (ANNs)
  2. Convolutional Neural Networks (CNNs)
  3. Recurrent Neural Networks (RNNs)
  4. Long Short-Term Memory Networks (LSTMs)
  5. Gated Recurrent Units (GRUs)
  6. Generative Adversarial Networks (GANs)
  7. Autoencoders (AEs)
  8. Variational Autoencoders (VAEs)
  9. Transformer Models
  10. BERT (Bidirectional Encoder Representations from Transformers)
  11. GPT (Generative Pretrained Transformer)
  12. Deep Belief Networks (DBNs)
  13. Radial Basis Function Networks (RBFNs)
  14. Siamese Networks
  15. Capsule Networks (CapsNets)
  16. Neural Turing Machines (NTMs)
  17. Deep Q-Networks (DQNs)

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