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Duration 14 hours
Course Outline
Foundations of AI
- Historical overview of AI
- Key definitions and terminology
- Comparing AI and human intelligence
- Emerging trends and future potential
Basics of Machine Learning
- Categories of machine learning: supervised, unsupervised, and reinforcement
- Essential ML algorithms
- ML workflow: spanning from data collection to model assessment
Data Stewardship
- Methods for data collection
- Data cleansing and preprocessing
- Data analysis and visualization techniques
AI Implementation
- Case studies on AI applications
- Sector-specific AI solutions
- AI integration in consumer products
Ethical Frameworks
- AI and the impact on employment
- Addressing bias and ensuring fairness in AI
- Concerns regarding privacy and security
- The trajectory of AI ethics
Practical Laboratory Project
- Python programming tasks
- Data analysis projects utilizing real-world datasets
- Construction of a basic ML model
Conclusion and Future Pathways
Requirements
- Comprehension of fundamental programming concepts
- Practical experience with Python programming
- Acquaintance with basic statistics and mathematical principles
Target Audience
- IT Professionals
Testimonials (1)
Step by step training with a lot of exercises. It was like a workshop and I am very glad about that.
