├── 1. Linear Regression.pdf ├── 10. Ensemble Techniques.pdf ├── 11. Boosting Algorithms.pdf ├── 12. kmeans.pdf ├── 14. Naive Bayes.pdf ├── 2. Regularization.pdf ├── 3. Logistic Regression.pdf ├── 4. In-Depth Logistic Regression.pdf ├── 5. Support Vector Machine Part 01.pdf ├── 6. Support Vector Machine Part 02.pdf ├── 7. ROC Curve.pdf ├── 8. Decision Tree ( Andrew NG lecture ).pdf ├── 9. Decision Tree.pdf └── README.md /1. Linear Regression.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ksdiwe/Machine-Learning-Notes/c0a1962c4be68df7e376629cc4dd043fcd74f0ef/1. Linear Regression.pdf -------------------------------------------------------------------------------- /10. Ensemble Techniques.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ksdiwe/Machine-Learning-Notes/c0a1962c4be68df7e376629cc4dd043fcd74f0ef/10. 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In-Depth Logistic Regression.pdf -------------------------------------------------------------------------------- /5. Support Vector Machine Part 01.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ksdiwe/Machine-Learning-Notes/c0a1962c4be68df7e376629cc4dd043fcd74f0ef/5. Support Vector Machine Part 01.pdf -------------------------------------------------------------------------------- /6. Support Vector Machine Part 02.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ksdiwe/Machine-Learning-Notes/c0a1962c4be68df7e376629cc4dd043fcd74f0ef/6. Support Vector Machine Part 02.pdf -------------------------------------------------------------------------------- /7. ROC Curve.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ksdiwe/Machine-Learning-Notes/c0a1962c4be68df7e376629cc4dd043fcd74f0ef/7. ROC Curve.pdf -------------------------------------------------------------------------------- /8. Decision Tree ( Andrew NG lecture ).pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ksdiwe/Machine-Learning-Notes/c0a1962c4be68df7e376629cc4dd043fcd74f0ef/8. Decision Tree ( Andrew NG lecture ).pdf -------------------------------------------------------------------------------- /9. Decision Tree.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ksdiwe/Machine-Learning-Notes/c0a1962c4be68df7e376629cc4dd043fcd74f0ef/9. Decision Tree.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Machine Learning Notes 2 | 3 | ## Linear Regression 4 | 1. Supervised and Unsupervised Machine Learning 5 | 2. Simple Linear Regression 6 | 3. Types of the cost function 7 | 4. Performance metrics 8 | 5. Overfitting and Underfitting 9 | ## Regularization 10 | 1. Ridge Regression (L2 regularization) 11 | 2. Lasso regression (L1 Regularization) 12 | 3. Elastic Net 13 | ## Logistic Regression 14 | 1. Logistics Regression 15 | 2. Cost function 16 | 3. Sigmoid function 17 | 4. Confusion Matrix 18 | 5. Recall 19 | 6. Accuracy 20 | 7. Precision 21 | 8. F-beta score 22 | ## Support Vector Machine: Part 01 23 | 1. Support Vector Machines (SVM) 24 | 2. Support Vector Classification (SVC) 25 | 3. Support Vector Regressor (SVR) 26 | 4. SVM Kernel 27 | 5. Linear Kernel 28 | 6. Polynomial Kernel 29 | 7. RBF Kernel 30 | 8. Sigmoid Kernel 31 | ## Support Vector Machine: Part 02 32 | 1. Mathematical Derivation of SVM 33 | 2. Hinge Loss 34 | 3. Lagrange Duality 35 | 4. Dual form 36 | 5. Quadratic Programming 37 | ## ROC Curve 38 | (Receiver Operating Characteristic Curve) 39 | ## Decision Tree by Andrew NG lecture 40 | 1. Decision Tree Learning 41 | 2. Entropy as a measure of Impurity 42 | 3. Choosing a split: Information Gain 43 | 4. One-Hot Coding 44 | 5. Continuous valued feature 45 | ## Decision Tree 46 | 1. Decision Tree 47 | 2. Gini Impurities 48 | 3. Entropy 49 | 4. Information Gain 50 | 5. Mathematical calculation on the Regression Problem 51 | 6. Pruning 52 | - Pre-Pruning 53 | - Post-Pruning 54 | 7. Algorithms in Decision Tree 55 | ## Ensemble Techniques 56 | 1. Ensemble Techniques 57 | 2. Voting Ensemble 58 | 3. Bagging 59 | 4. Random Forest 60 | ## Boosting Algorithms 61 | 1. Boosting 62 | 2. Adaptive Boosting 63 | ## Clustering 64 | * K-means Algorithm 65 | * Hierarchical Clustering 66 | * DBSCAN 67 | ## Naive Bayes 68 | --------------------------------------------------------------------------------