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Course Outline
Introduction to AI
- Historical development of AI
- Key definitions and terminology
- Comparison between AI and human intelligence
- Emerging trends and future potential
Machine Learning Fundamentals
- Categorization of machine learning: supervised, unsupervised, and reinforcement
- Essential ML algorithms
- The ML lifecycle: from data gathering to model assessment
Data Management
- Techniques for data collection
- Cleaning and preprocessing of data
- Analyzing and visualizing data
AI in the Real World
- Case studies demonstrating AI usage
- Sector-specific AI solutions
- Integration of AI in consumer-facing products
Ethical Considerations
- AI's effect on employment dynamics
- Addressing bias and ensuring fairness in AI
- Privacy and security concerns
- The future landscape of AI ethics
Practical Lab Project
- Python-based programming tasks
- Data analysis projects utilizing real-world datasets
- Construction of a basic ML model
Wrap-up and Future Directions
Requirements
- A solid grasp of fundamental programming concepts
- Practical experience with Python programming
- Basic knowledge of statistics and mathematics
Target Audience
- IT Professionals
14 Hours
Testimonials (1)
Step by step training with a lot of exercises. It was like a workshop and I am very glad about that.
