یادگیری ماشین نظارت شده

1.1. Introduction to Machine Learning

10:26

2.1. ?What is Machine Learning

09:51

3.1. Learning Types

13:06

1.2. Introduction to Linear Regression

13:55

2.2. Gradient Descent

17:58

3.2. Nonlinear Regression

16:08

4.2. Bias & Variance

18:20

5.2. Regularization

21:42

6.2. Introduction to Sklearn(Programing)

08:57

7.2. Pre-process 01(Programing)

14:08

8.2. Pre-process 02(Programing)

13:37

9.2. Pre-process 03(Programing)

16:17

10.2. Pre-process 04(Programing)

15:06

11.2. Pre-process 05(Programing)

16:36

12.2. Pre-process 06(Programing)

17:28

13.2. Regression 01(Programing)

14:44

14.2. Regression 02(Programing)

15:28

15.2. Regression 03(Programing)

18:39

16.2. Regression 04(Programing)

18:00

1.3. KNN

16:26

2.3. KNN decision boundary

08:37

3.3. KNN 01(Programing)

15:25

4.3. KNN 02(Programing)

12:57

1.4. Evaluation Metrics

15:08

2.4. Metric 01(Programing)

17:56

3.4. Metric 02(Programing)

15:58

1.5. Decision Tree

13:34

2.5. Entropy

14:31

3.5. ID3 algorithm

16:53

4.5. C4.5 algorithm

23:37

5.5. Decision Tree(Programing)

18:08

1.6. Multiclass

16:15

1.7. Bayesian Networks

16:03

2.7. Inference in Bayesian Networks

14:54

3.7. Naive Bayes Classifier

14:05

4.7. Naive Bayes classifier Example

15:00

5.7. Hidden Markov Model

19:56

1.8. Gaussian Naive Bayes

12:51

2.8. Gaussian Naive Bayes Parametric

15:30

3.8. Naive Bayesian 01(Programing)

15:16

4.8. Naive Bayesian 02(Programing)

17:50

1.9. Logistic Regression

11:34

2.9. Formalism

09:20

3.9. Roc Curve

13:28

4.9. Logistic Regression(Programing)

20:43

1.10. SVM

27:52

2.10. Svm Example

18:03

3.10. Kernel Functions

21:25

4.10. Soft Margin in Svm

21:23

5.10. SVM(Programing)

19:33

1.11. Ensemble of Classifiers

12:14

2.11. Stacking & Bagging

14:43

3.11. Boosting & Adaboost

23:18

4.11. Ensemble(Programing)

16:37