Course Content
Modules 1: Introduction to Artificial Intelligence
0/6
Introduction to Artificial Intelligence
03:18
Definition of AI
03:18
Brief history of AI development
03:28
Types of AI: weak vs strong, narrow vs general
03:36
Quiz
00:10:00
Stimulation exercise
00:00
Module 2: Key Concepts in AI
0/11
Machine Learning
03:14
Supervised learning
03:31
Unsupervised learning
03:46
Reinforcement learning
03:46
Deep Learning
03:17
Natural Language Processing (NLP)
03:44
Robotics
02:59
Neural networks
03:02
Knowledge representation
02:58
Quiz
00:10:00
Stimulation exercise
00:00
Module 3: Machine Learning
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Machine Learning
13:20
Introduction to machine learning: supervised, unsupervised, and reinforcement learning
02:48
Supervised learning algorithms: linear regression, logistic regression, support vector machines
03:46
Unsupervised learning algorithms: k-means clustering, hierarchical clustering, principal component analysis
03:13
Deep learning frameworks: TensorFlow, PyTorch
03:41
Quiz
00:10:00
Stimulation Exercise
00:00
Module 4: Neural Networks and Deep Learning
0/5
Neural Networks and Deep Learning
13:04
Introduction to neural networks: perceptron, multilayer perceptron, backpropagation
02:52
Convolutional neural networks and recurrent neural networks
03:07
Short Quiz
Stimulation Exercise 4
00:00
Module 5: Natural Language Processing
0/6
Natural Language Processing
03:36
Introduction to NLP: syntax, semantics, pragmatics
03:06
Text preprocessing and normalization
03:00
Word embeddings and language models
02:56
Short Quiz
Stimulation Exercise 5
00:00
Module 6: Computer Vision
0/3
Computer Vision
05:27
Short Quiz
00:10:00
Stimulation Exercise 6
00:00
Module 7: Intelligent Agents
0/3
Definition and types of agents
13:12
Short Quiz
Stimulation Exercise 7
00:00
Module 8: Problem Solving and Search
0/5
Definition and types of problems
03:29
Problem-solving strategies: brute force, divide and conquer, hill climbing
02:57
Heuristics and optimization techniques
03:28
Short Quiz
Stimulation Exercise 8
00:00
Module 9: Knowledge Representation and Reasoning
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Knowledge Representation and Reasoning
02:08
Types of knowledge representation: propositional logic, first-order logic, semantic networks
04:29
Reasoning techniques: forward and backward chaining, resolution, rule-based systems
03:09
Short Quiz
Stimulation Exercise 9
00:00
Module 10: Common Techniques Used in Problem-Solving
0/5
Regression analysis
03:00
Classification algorithms
03:22
Clustering algorithms
03:38
Short Quiz
Stimulation Exercise 10
00:00
Module 11: Applications of AI
0/5
Applications of AI in Healthcare
01:47
Applications of AI in Finance
03:16
Applications of AI in Manufacturing
03:02
Short Quiz
Stimulation Exercise 11
00:00
Module 12: Benefits and Challenges of Implementing AI
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Benefits of Implementing AI
02:11
Challenges of Implementing AI
01:52
Advantages of AI adoption
04:16
Barriers to AI implementation
03:46
Mitigating risks and challenges
04:22
Short Quiz
Stimulation Exercise 12
00:00
Module 13: Ethical Considerations
0/6
Ethical Considerations
07:44
Potential ethical issues with AI
03:39
Guidelines for ethical AI development
04:07
Importance of transparency and accountability
03:39
Short Quiz
Stimulation Exercise 13
00:00
Module 14: Real-world Examples and Case Studies
0/3
Case studies of successful AI implementations in different industries
08:31
Short Quiz
Stimulation Exercise 14
00:00
Practical Questions
0/1
40 Practical Questions
02:00:00
Final Exams
0/1
50 exams Question
00:02:00
Introduction to Artificial Intelligence (AI)
About Lesson

Stimulation exercise

You are a data scientist working for a security company. Your task is to develop a computer vision system that can detect and recognize faces in real-time video feeds to enhance security measures. You will need to use various computer vision techniques and concepts to achieve this goal.

Exercise Files
Stimulation Exercise 6.pdf
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