Data Science Algorithms

1. Supervised Learning

1.1 Classification

• Logistic Regression
• Support Vector Machines (SVM)
• Decision Trees
• Random Forest
• K-Nearest Neighbors (KNN)
• Naive Bayes
  • Gaussian Naive Bayes
  • Multinomial Naive Bayes
  • Bernoulli Naive Bayes
• XGBoost
• LightGBM
• CatBoost
• AdaBoost

1.2 Regression

• Linear Regression
• Polynomial Regression
• Ridge Regression (L2)
• Lasso Regression (L1)
• Elastic Net
• Decision Tree Regression
• Random Forest Regression
• Support Vector Regression (SVR)
• Gradient Boosting Regression

2. Unsupervised Learning

2.1 Clustering

• K-Means
• Hierarchical Clustering
  • Agglomerative
  • Divisive
• DBSCAN
• Mean Shift
• Gaussian Mixture Models
• Spectral Clustering
• OPTICS

2.2 Dimensionality Reduction

• Principal Component Analysis (PCA)
• t-SNE
• UMAP
• Linear Discriminant Analysis (LDA)
• Factor Analysis
• Autoencoders
• Non-negative Matrix Factorization (NMF)

2.3 Association Rule Learning

• Apriori
• Eclat
• FP-Growth

3. Deep Learning

3.1 Neural Networks

• Feedforward Neural Networks
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
  • LSTM
  • GRU
• Transformers
• Graph Neural Networks
• Generative Adversarial Networks (GANs)

3.2 Deep Learning Architectures

• ResNet
• VGG
• BERT
• GPT
• U-Net
• Inception
• EfficientNet

4. Reinforcement Learning

4.1 Value-Based Methods

• Q-Learning
• Deep Q-Network (DQN)
• SARSA

4.2 Policy-Based Methods

• Policy Gradient
• Actor-Critic
• Proximal Policy Optimization (PPO)

5. Time Series Analysis

5.1 Classical Methods

• ARIMA
• SARIMA
• Exponential Smoothing
  • Simple
  • Double
  • Triple (Holt-Winters)
• Prophet

5.2 Modern Approaches

• LSTM for Time Series
• CNN for Time Series
• Temporal Convolutional Networks
• Neural Prophet

6. Natural Language Processing

6.1 Text Processing

• TF-IDF
• Word2Vec
• GloVe
• FastText
• BERT Embeddings
• Doc2Vec

6.2 Topic Modeling

• Latent Dirichlet Allocation (LDA)
• Non-negative Matrix Factorization (NMF)
• Latent Semantic Analysis (LSA)

7. Ensemble Methods

7.1 Bagging

• Random Forest
• Extra Trees
• Bagging Classifier/Regressor

7.2 Boosting

• AdaBoost
• Gradient Boosting
• XGBoost
• LightGBM
• CatBoost

8. Optimization Algorithms

8.1 Gradient-Based

• Gradient Descent
• Stochastic Gradient Descent
• Adam
• RMSprop
• AdaGrad

8.2 Nature-Inspired

• Genetic Algorithms
• Particle Swarm Optimization
• Ant Colony Optimization
• Simulated Annealing

9. Model Evaluation and Feature Engineering

10. Algorithm Selection, Hyperparameter Tuning, and Deployment

11. Advanced Performance Analysis and Model Interpretability

12. Edge Cases, System Integration, Monitoring, and Ethics

Data Science Algorithms Articles

Edge Cases, System Integration, Monitoring, and Ethics

Edge Cases, System Integration, Monitoring, and Ethics

Advanced Performance Analysis and Model Interpretability

Advanced Performance Analysis and Model Interpretability

Algorithm Selection, Hyperparameter Tuning, and Deployment

Algorithm Selection, Hyperparameter Tuning, and Deployment

Model Evaluation and Feature Engineering

Model Evaluation and Feature Engineering

Ensemble Methods and Optimization Algorithms

Ensemble Methods and Optimization Algorithms

Time Series Analysis and Natural Language Processing

Time Series Analysis and Natural Language Processing

Generative Models and Reinforcement Learning

Generative Models and Reinforcement Learning

Deep Learning Algorithms and Architectures

Deep Learning Algorithms and Architectures

Dimensionality Reduction and Association Rule Learning

Dimensionality Reduction and Association Rule Learning

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