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Course Outline
Introduction to AI
- History of AI
- Definitions and terminology
- AI vs. human intelligence
- Future trends and potential
Machine Learning Basics
- Types of machine learning: supervised, unsupervised, reinforcement
- Key ML algorithms
- ML workflow: from data collection to model evaluation
Data Management
- Data collection techniques
- Data cleaning and preprocessing
- Data analysis and visualization
AI in Practice
- Case studies of AI applications
- Industry-specific AI solutions
- AI in consumer products
Ethical Considerations
- AI and job displacement
- Bias and fairness in AI
- Privacy and security issues
- Future of AI ethics
Lab Project
- Python programming assignments
- Data analysis projects using real-world datasets
- Development of a simple ML model
Summary and Next Steps
Requirements
- An understanding of basic programming concepts
- Experience with Python programming
- Familiarity with basic statistics and mathematics
Audience
- IT Professionals
14 Hours
Testimonials (2)
examples and exercises
Kamil
Course - Introduction to Data Science and AI using Python
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