/Machine-Learning-Notes

Machine Learning Notes

Linear Regression

  1. Supervised and Unsupervised Machine Learning
  2. Simple Linear Regression
  3. Types of the cost function
  4. Performance metrics
  5. Overfitting and Underfitting

Regularization

  1. Ridge Regression (L2 regularization)
  2. Lasso regression (L1 Regularization)
  3. Elastic Net

Logistic Regression

  1. Logistics Regression
  2. Cost function
  3. Sigmoid function
  4. Confusion Matrix
  5. Recall
  6. Accuracy
  7. Precision
  8. F-beta score

Support Vector Machine: Part 01

  1. Support Vector Machines (SVM)
  2. Support Vector Classification (SVC)
  3. Support Vector Regressor (SVR)
  4. SVM Kernel
  5. Linear Kernel
  6. Polynomial Kernel
  7. RBF Kernel
  8. Sigmoid Kernel

Support Vector Machine: Part 02

  1. Mathematical Derivation of SVM
  2. Hinge Loss
  3. Lagrange Duality
  4. Dual form
  5. Quadratic Programming

ROC Curve

(Receiver Operating Characteristic Curve)

Decision Tree by Andrew NG lecture

  1. Decision Tree Learning
  2. Entropy as a measure of Impurity
  3. Choosing a split: Information Gain
  4. One-Hot Coding
  5. Continuous valued feature

Decision Tree

  1. Decision Tree
  2. Gini Impurities
  3. Entropy
  4. Information Gain
  5. Mathematical calculation on the Regression Problem
  6. Pruning
  • Pre-Pruning
  • Post-Pruning
  1. Algorithms in Decision Tree

Ensemble Techniques

  1. Ensemble Techniques
  2. Voting Ensemble
  3. Bagging
  4. Random Forest

Boosting Algorithms

  1. Boosting
  2. Adaptive Boosting

Clustering

  • K-means Algorithm
  • Hierarchical Clustering
  • DBSCAN

Naive Bayes