Programming Foundations of Classification and Regression,Part 1

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4:24:10

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01 - Introduction.mp4

04:36

02 - Topics.mp4

00:28

03 - 1.1 Environment Installation.mp4

06:27

04 - 1.2 Three Things You Can do with NumPy and matplotlib.mp4

11:16

05 - 1.3 Three Things You Can Do with Pandas.mp4

10:37

06 - 1.4 Three Things You Can Do with scikit-learn and Friends.mp4

08:51

07 - Topics.mp4

00:33

08 - 2.1 Probability.mp4

06:04

09 - 2.2 Distributions.mp4

07:27

10 - 2.3 Linear Combinations.mp4

18:22

11 - 2.4 Geometry, Part 1.mp4

12:55

12 - 2.5 Geometry, Part 2.mp4

13:14

13 - 2.6 Geometry, Part 3.mp4

05:30

14 - 2.7 When Computers and Math Meet.mp4

07:16

15 - Topics.mp4

00:27

16 - 3.1 Setup and the Iris Dataset.mp4

07:57

17 - 3.2 Accuracy.mp4

04:27

18 - 3.3 k-Nearest Neighbors.mp4

07:01

19 - 3.4 Train Test Splitting and Fitting k-NN.mp4

11:43

20 - 3.5 Naive Bayes.mp4

11:23

21 - Topics.mp4

00:27

22 - 4.1 Learning Evaluation.mp4

04:25

23 - 4.2 Resource Evaluation - Time.mp4

06:36

24 - 4.3 Resource Evaluation - Memory.mp4

07:26

25 - 4.4 Scripts.mp4

12:54

26 - Topics.mp4

00:25

27 - 5.1 Setup and the Diabetes Dataset.mp4

09:29

28 - 5.2 Measures of Center.mp4

05:25

29 - 5.3 k-Nearest Neighbors for Regression.mp4

08:28

30 - 5.4 Linear Regression, Part 1.mp4

14:40

31 - 5.5 Linear Regression, Part 2.mp4

05:14

32 - Topics.mp4

00:21

33 - 6.1 Optimization, Part 1.mp4

12:06

34 - 6.2 Optimization, Part 2.mp4

06:47

35 - 6.3 Learning Performance.mp4

04:10

36 - 6.4 Resource Evaluation.mp4

07:41

37 - Summary.mp4

01:02

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