Artificial Intelligence and Machine Learning Fundamentals

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7:46:22

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01.01-course overview .mp4

10:40

01.02-installation and setup .mp4

04:30

01.03-lesson overview .mp4

03:21

01.04-introduction to ai and machine learning.mp4

08:13

01.05-how does ai solve real world problems .mp4

14:22

01.06-fields and applications of artificial intelligence.mp4

08:30

01.07-ai tools and learning models .mp4

06:45

01.08-the role of python in artificial intelligence .mp4

14:17

01.09-a brief introduction to the numpy library.mp4

06:58

01.10-python for game ai .mp4

11:52

01.11-breadth first search and depth first search .mp4

13:58

01.12-lesson summary .mp4

02:06

02.01-lesson overview.mp4

11:04

02.02-heuristics.mp4

12:49

02.03-tic-tac-toe .mp4

10:04

02.04-pathfinding with the a algorithm .mp4

07:35

02.05-introducing the a algorithm .mp4

19:29

02.06-game ai with the minmax algorithm .mp4

09:34

02.07-game ai with alpha-beta pruning.mp4

08:17

02.08-lesson summary .mp4

01:23

03.01-lesson overview .mp4

02:35

03.02-linear regression with one variable .mp4

13:15

03.03-fitting a model on data with scikit-learn.mp4

13:51

03.04-linear regression with multiple variables.mp4

10:41

03.05-preparing data for protection.mp4

09:15

03.06-polynomial and support vector regression .mp4

13:37

03.07-lesson summary .mp4

01:31

04.01-the fundamentals of classification part 1.mp4

00:53

04.02-the fundamentals of classification part 2.mp4

06:36

04.03-the k-nearest neighbor classifier.mp4

12:03

04.04-classification with support vector machines.mp4

13:27

04.05-lesson summary.mp4

13:08

05.01-lesson overview.mp4

01:18

05.02-introduction to decision trees.mp4

14:03

05.03-entropy.mp4

07:30

05.04-gini impurity.mp4

11:08

05.05-precision and recall.mp4

15:34

05.06-random forest classifier.mp4

09:04

05.07-random forest classification using scikit-learn.mp4

06:42

05.08-lesson summary.mp4

01:31

06.01-lesson overview.mp4

01:22

06.02-introduction to clustering.mp4

11:28

06.03-the k-means algorithm.mp4

13:38

06.04-mean shift algorithm.mp4

12:58

06.05-lesson summary.mp4

01:31

07.01-lesson overview.mp4

01:14

07.02-tensorflow for python.mp4

13:06

07.03-introduction to neural networks.mp4

15:39

07.04-forward and backward propagation.mp4

14:11

07.05-training the tensorflow model.mp4

09:02

07.06-deep learning.mp4

05:35

07.07-lesson summary.mp4

03:09

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