Artificial Intelligence and Machine Learning Course

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EDUCBA Bridging the Gap

11:45:47

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1. Introduction to Artificial Intelligence.mp4

08:15

2. Definition of Artificial Intelligence.mp4

07:13

3. Intelligent Agents.mp4

06:36

4. Information on State Space Search.mp4

07:07

5. Graph theory on state space search.mp4

09:28

6. Solution for State Space Search.mp4

08:13

7. FSM.mp4

08:46

8. BFS on Graph.mp4

07:26

9. DFS algo.mp4

10:07

10. DFS with iterative deepening.mp4

08:47

11. Backtracking algo.mp4

11:17

12. Trace backtracking on graph part 1.mp4

06:57

13. Trace backtracking on graph part 2.mp4

09:39

14. Summary state space search.mp4

04:39

15. Heuristic search overview.mp4

08:03

16. Heuristic calculation technique part 1.mp4

06:28

17. Heuristic calculation technique part 2.mp4

06:27

18. Simple hill climbing.mp4

07:43

19. Best first search algo.mp4

07:23

20. Tracing best first search-1.mp4

11:41

21. Best first search continue.mp4

05:35

22. Admissibility-1.mp4

12:18

23. Mini-max.mp4

12:11

24. Two ply min max.mp4

07:50

25. Alpha beta pruning.mp4

09:48

26. Machine learning overview.mp4

08:44

27. Perceptron learning.mp4

14:16

28. Perceptron with linearly separable.mp4

07:07

29. Backpropagation with multilayer neuron.mp4

07:44

30. W for hidden node and backpropagation algo.mp4

09:56

31. Backpropagation algorithm explained.mp4

12:07

32. Backpropagation calculation part01.mp4

07:12

33. Backpropagation calculation part02.mp4

07:09

34. Updation of weight and cluster.mp4

07:43

35. K-Means cluster‚NNalgo and appliaction of machine learning.mp4

06:21

36. Logics reasoning overview propositional calculas part 1.mp4

07:06

37. Logics reasoning overview propositional calculas part 2.mp4

05:05

38. Propotional calculus.mp4

07:51

39. Predicate calculus.mp4

06:17

40. First order predicate calculus.mp4

07:33

41. modus ponus,tollens.mp4

08:11

42. Unification and deduction process.mp4

07:50

43. Resolution refutation.mp4

11:05

44. Resolution refutation in detail.mp4

08:58

45. Resolution refutation example-2 convert into clause.mp4

07:40

46. Resoultion refutation example-2 apply refutation.mp4

07:03

47. Unification substitution andskolemization.mp4

07:20

48. Prolog overview some part of reasoning.mp4

12:06

49. Model based and CBR reasoning.mp4

05:00

50. Production system.mp4

07:42

51. Trace of production system.mp4

07:17

52. Knight tour prob in chessboard.mp4

09:12

53. Goal driven data driven production system part 1.mp4

05:33

54. Goal driven data driven production system part 2.mp4

07:19

55. Goal driven Vs data driven and inserting and removing facts.mp4

07:05

56. Defining rules and commands.mp4

08:47

57. CLIPS installation and clipstutorial 1.mp4

07:57

58. CLIPS tutorial 2.mp4

07:02

59. CLIPS tutorial 3.mp4

07:25

60. CLIPS tutorial 4.mp4

06:39

61. CLIPS tutorial 5 part01.mp4

05:28

62. CLIPS tutorial 5 part02.mp4

03:27

63. Tutorial 6.mp4

02:33

64. CLIPS tutorial 7.mp4

06:18

65. CLIPS tutorial 8.mp4

06:03

66. Variable in pattern tutorial 9.mp4

05:12

67. Tutorial 10.mp4

05:11

68. More on wildcardmatching part01.mp4

07:44

69. More on wildcardmatching part02.mp4

05:32

70. More on variables.mp4

08:12

71. Deffacts and deftemplates part01.mp4

05:31

72. Deffacts and deftemplates part02.mp4

07:10

73. Template indetail part1.mp4

07:03

74. Not operator.mp4

06:16

75. Forall and exists part01.mp4

05:41

76. Forall and exists part02.mp4

05:18

77. Truth and control.mp4

06:45

78. Tutorial 12.mp4

04:36

79. Intelligent agent.mp4

06:36

80. Simple reflex agent.mp4

06:45

81. Simple reflex agent with internal state.mp4

06:11

82. Goal based agent.mp4

04:12

83. Utility based agent.mp4

08:09

84. Basics of utility theory.mp4

08:04

85. Maximum expected utility.mp4

07:02

86. Decision theory and decision network.mp4

09:01

87. Reinforcement learning.mp4

07:17

88. MDPand DDN.mp4

11:10

89. Basics of set theory part 1.mp4

05:53

90. Basics of set theory part 2.mp4

06:25

91. Probability distribution.mp4

08:54

92. Baysian rule for conditional probability.mp4

11:27

93. Examples of Bayes Theorm.mp4

05:22

Description

Basic ideas and techniques in the design of intelligent computer systems.

What You'll Learn?

Identify potential areas of applications of AI
Basic ideas and techniques in the design of intelligent computer systems
Statistical and decision-theoretic modeling paradigm
How to build agents that exhibit reasoning and learning
Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.

Who is this for?

The target audience for this course includes students and professionals who are interested in learning robotics and biometrics. This Machine learning training is also meant for people who are very keen on learning Artificial Intelligence.

What You Need to Know?

The topics included in this topic will be related to probability theorem and linear algebra. So a basic knowledge of statistics and mathematics is an added advantage to take up this Machine learning course

More details

Description
Artificial Intelligence has been used in wide range of fields these days. For example medical diagnosis, robots, remote sensing, etc. Artificial intelligence is around us in many ways but we donâ€™t realize it. For example, the ATM which we are using is an artificial intelligence machine learning training. Few of the advantages of using artificial intelligence is listed below
Greater precision and accuracy can be achieved through AI
These machines do not get affected by the planetary environment or atmosphere
Robots can be programmed to do the works which are difficult for the human beings to complete
AI will open up doors to new technological breakthroughs
As they are machines they donâ€™t stop for sleep or food or rest. They just need some source of energy to work
Fraud detection becomes easier with artificial intelligence
Using AI the time-consuming tasks can be done more efficiently
Dangerous tasks can be done using AI machines as it affects only the machines and not the human beings
Artificial Intelligence has become the centrepiece of strategic decision making for organizations. It is disrupting the way industries function - from sales and marketing to finance and HR, companies are betting on AI to give them a competitive edge. This course is a thoughtfully created course designed specifically for business people and does not require any programming. Through this course you will learn about the current state of AI, how it's disrupting businesses globally and in diverse fields, how it might impact your current role and what you can do about it. This course also dives into the various building blocks of AI and why it's necessary for you to have a high-level overview of these topics in today's data-driven world.
Who this course is for:
The target audience for this course includes students and professionals who are interested in learning robotics and biometrics. This Machine learning training is also meant for people who are very keen on learning Artificial Intelligence.

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Machine Learning

Artificial Intelligence

EDUCBA Bridging the Gap

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