- Supervised and Unsupervised Machine Learning
- Simple Linear Regression
- Types of the cost function
- Performance metrics
- Overfitting and Underfitting
- Ridge Regression (L2 regularization)
- Lasso regression (L1 Regularization)
- Elastic Net
- Logistics Regression
- Cost function
- Sigmoid function
- Confusion Matrix
- Recall
- Accuracy
- Precision
- F-beta score
- Support Vector Machines (SVM)
- Support Vector Classification (SVC)
- Support Vector Regressor (SVR)
- SVM Kernel
- Linear Kernel
- Polynomial Kernel
- RBF Kernel
- Sigmoid Kernel
- Mathematical Derivation of SVM
- Hinge Loss
- Lagrange Duality
- Dual form
- Quadratic Programming
(Receiver Operating Characteristic Curve)
- Decision Tree Learning
- Entropy as a measure of Impurity
- Choosing a split: Information Gain
- One-Hot Coding
- Continuous valued feature
- Decision Tree
- Gini Impurities
- Entropy
- Information Gain
- Mathematical calculation on the Regression Problem
- Pruning
- Pre-Pruning
- Post-Pruning
- Algorithms in Decision Tree
- Ensemble Techniques
- Voting Ensemble
- Bagging
- Random Forest
- Boosting
- Adaptive Boosting
- K-means Algorithm
- Hierarchical Clustering
- DBSCAN