- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Support Vector Regression (SVR)
- Decision Tree Regression
- Random Forest Regression
- Evaluating Regression Models Performance
- Logistic Regression
- K-Nearest Neighbors (K-NN)
- Support Vector Machine (SVM)
- Kernel SVM
- Naive Bayes
- Decision Tree Classification
- Random Forest Classification
- Evaluating Classification Models Performance
- K-Means Clustering
- Hierarchical Clustering
- Apriori
- Eclat
- Upper Confidence Bound (UCB)
- Thompson Sampling
- Artificial Neural Networks
- Convolutional Neural Networks
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- Kernel PCA
- Model Selection (k-Fold Cross Validation & Grid Search)
- XGBoost