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
