Developing Classification and Regression Systems Part 3

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4:37:13

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1 -Introduction.mp4

03:33

2 - Topics.mp4

00:27

3 - 1.1 Revisiting Classification.mp4

12:50

4 - 1.2 Decision Trees I.mp4

16:11

5 - 1.3 Decision Trees II.mp4

14:28

6 - 1.4 Support Vector Classifiers I.mp4

07:44

7 - 1.5 Support Vector Classifiers II.mp4

12:55

8 - Topics.mp4

00:27

9 - 2.1 Logistic Regression I.mp4

08:19

10 - 2.2 Logistic Regression II.mp4

12:11

11 - 2.3 Discriminant Analysis I.mp4

16:35

12 - 2.4 Discriminant Analysis II.mp4

15:37

13 - 2.5 Bias and Variance of Classifiers.mp4

02:16

14 - 2.6 Comparing Classifiers.mp4

05:28

15 - Topics.mp4

00:33

16 - 3.1 Penalized Regression I.mp4

15:24

17 - 3.2 Penalized Regression II.mp4

04:38

18 - 3.3 Piecewise Constant Regression.mp4

06:53

19 - 3.4 Regression Trees.mp4

07:06

20 - 3.5 Bias and Variance of Regressors.mp4

02:48

21 - 3.6 Comparing Regressors.mp4

05:54

22 - Topics.mp4

00:31

23 - 4.1 Overview of Feature Engineering.mp4

02:17

24 - 4.2 Feature Scaling.mp4

09:43

25 - 4.3 Discretization.mp4

07:29

26 - 4.4 Categorical Coding.mp4

09:00

27 - 4.5 Interactions.mp4

16:07

28 - 4.6 Target Manipulations.mp4

13:46

29 - Topics.mp4

00:32

30 - 5.1 Models, Parameters, and Hyperparameters.mp4

06:11

31 - 5.2 Tuning Hyperparameters.mp4

16:33

32 - 5.3 Nested Cross-validation.mp4

11:01

33 - 5.4 Pipelines.mp4

06:12

34 - 5.5 Tuning Pipelines.mp4

04:26

35 -Summary.mp4

01:08

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