Contents

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