یادگیری ماشین نظارت شده

1.1. Introduction to Machine Learning

00:10′

2.1. ?What is Machine Learning

00:09′

3.1. Learning Types

00:13′

1.2. Introduction to Linear Regression

00:13′

2.2. Gradient Descent

00:17′

3.2. Nonlinear Regression

00:16′

4.2. Bias & Variance

00:18′

5.2. Regularization

00:21′

6.2. Introduction to Sklearn(Programing)

00:08′

7.2. Pre-process 01(Programing)

00:14′

8.2. Pre-process 02(Programing)

00:13′

9.2. Pre-process 03(Programing)

00:16′

10.2. Pre-process 04(Programing)

00:15′

11.2. Pre-process 05(Programing)

00:16′

12.2. Pre-process 06(Programing)

00:17′

13.2. Regression 01(Programing)

00:14′

14.2. Regression 02(Programing)

00:15′

15.2. Regression 03(Programing)

00:18′

16.2. Regression 04(Programing)

00:18′

1.3. KNN

00:16′

2.3. KNN decision boundary

00:08′

3.3. KNN 01(Programing)

00:15′

4.3. KNN 02(Programing)

00:12′

1.4. Evaluation Metrics

00:15′

1.5. Decision Tree

00:13′

2.5. Entropy

00:14′

3.5. ID3 algorithm

00:16′

4.5. C4.5 algorithm

00:23′

5.5. پیاده سازی درخت تصمیم

00:18′

6.5. بررسی معیارهای ارزیابی در درخت تصمیم

00:17′

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

00:15′

1.6. Multiclass

00:16′

1.7. Bayesian Networks

00:16′

2.7. Inference in Bayesian Networks

00:14′

3.7. Naive Bayes Classifier

00:14′

4.7. Naive Bayes classifier Example

00:15′

5.7. Hidden Markov Model

00:19′

1.8. Gaussian Naive Bayes

00:12′

2.8. Gaussian Naive Bayes Parametric

00:15′

3.8. Naive Bayesian 01(Programing)

00:15′

4.8. Naive Bayesian 02(Programing)

00:17′

1.9. Logistic Regression

00:11′

2.9. Formalism

00:09′

3.9. Roc Curve

00:13′

4.9. Logistic Regression(Programing)

00:20′

1.10. SVM

00:27′

2.10. Svm Example

00:18′

3.10. Kernel Functions

00:21′

4.10. Soft Margin in Svm

00:21′

5.10. SVM(Programing)

00:19′

1.11. Ensemble of Classifiers

00:12′

2.11. Stacking & Bagging

00:14′

3.11. Boosting & Adaboost

00:23′

4.11. Ensemble(Programing)

00:16′