یادگیری ماشین نظارت شده

1.1. Introduction to Machine Learning

10

2.1. ?What is Machine Learning

09

3.1. Learning Types

13

1.2. Introduction to Linear Regression

13

2.2. Gradient Descent

17

3.2. Nonlinear Regression

16

4.2. Bias & Variance

18

5.2. Regularization

21

6.2. Introduction to Sklearn(Programing)

08

7.2. Pre-process 01(Programing)

14

8.2. Pre-process 02(Programing)

13

9.2. Pre-process 03(Programing)

16

10.2. Pre-process 04(Programing)

15

11.2. Pre-process 05(Programing)

16

12.2. Pre-process 06(Programing)

17

13.2. Regression 01(Programing)

14

14.2. Regression 02(Programing)

15

15.2. Regression 03(Programing)

18

16.2. Regression 04(Programing)

18

1.3. KNN

16

2.3. KNN decision boundary

08

3.3. KNN 01(Programing)

15

4.3. KNN 02(Programing)

12

1.4. Evaluation Metrics

15

1.5. Decision Tree

13

2.5. Entropy

14

3.5. ID3 algorithm

16

4.5. C4.5 algorithm

23

5.5. پیاده سازی درخت تصمیم

18

6.5. بررسی معیارهای ارزیابی در درخت تصمیم

17

7.5. بررسی ماتریس درهم ریختگی و نمودار ROC در درخت تصمیم

15

1.6. Multiclass

16

1.7. Bayesian Networks

16

2.7. Inference in Bayesian Networks

14

3.7. Naive Bayes Classifier

14

4.7. Naive Bayes classifier Example

15

5.7. Hidden Markov Model

19

1.8. Gaussian Naive Bayes

12

2.8. Gaussian Naive Bayes Parametric

15

3.8. Naive Bayesian 01(Programing)

15

4.8. Naive Bayesian 02(Programing)

17

1.9. Logistic Regression

11

2.9. Formalism

09

3.9. Roc Curve

13

4.9. Logistic Regression(Programing)

20

1.10. SVM

27

2.10. Svm Example

18

3.10. Kernel Functions

21

4.10. Soft Margin in Svm

21

5.10. SVM(Programing)

19

1.11. Ensemble of Classifiers

12

2.11. Stacking & Bagging

14

3.11. Boosting & Adaboost

23

4.11. Ensemble(Programing)

16