Contents
- Introduction to Machine Learning
- Regression Algorithms
- Linear Regression
- Polynomial Regression
- Ridge Regression
- Lasso Regression
- Elastic Net Regression
- Support Vector Regression
- Classification Algorithms
- Logistic Regression
- Decision Trees
- Random Forests
- Support Vector Machines (SVMs)
- Naive Bayes
- K-Nearest Neighbors (KNN):
- Clustering Algorithms
- K-Means
- Hierarchical Clustering
- Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
- Gaussian Mixture Models (GMMs)
- Spectral Clustering:
- Fuzzy C-Means
- Association Rule Learning Algorithms
- Apriori
- FP-Growth
- ECLAT
- CARMA (Clustering and Association Rule Mining Algorithm):
- RuleFit
- Sequential Pattern Mining
- Dimensionality Reduction Algorithms
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- t-SNE (t-Distributed Stochastic Neighbor Embedding)
- Isomap (Isometric Mapping):
- Non-negative Matrix Factorization (NMF)
- Autoencoders
- Ensemble Methods
- Bagging:
- Boosting:
- Random Forest
- AdaBoost:
- Gradient Boosting
- Stacking
- Deep Learning Algorithms
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory (LSTM) Networks
- Generative Adversarial Networks (GANs):
- Deep Reinforcement Learning:
- Deep Belief Networks (DBNs)
- Transformer Networks
- Self-Organizing Maps (SOMs)
- Capsule Networks
- Radical Basis Function Networks (RBFNs)
- Reinforcement Learning Algorithms
- Q-Learning
- SARSA
- Deep Q-Networks (DQNs)
- Policy Gradient Methods
- Actor-Critic Methods:
- Monte Carlo Methods