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Duration 14 hours
Course Outline
Foundations of Artificial Intelligence
- Historical evolution and development
- Core definitions and technical terminology
- Distinguishing artificial intelligence from human cognition
- Emerging trends and long-term potential
Machine Learning Fundamentals
- Methodologies: supervised, unsupervised, and reinforcement learning
- Essential machine learning algorithms
- Operational workflow: from data acquisition to model validation
Data Governance and Management
- Strategies for data acquisition
- Data refinement and preprocessing procedures
- Analytical techniques and data visualization methods
Practical Applications of AI
- Analysis of real-world AI deployment cases
- Sector-specific artificial intelligence solutions
- Integration of AI in consumer-facing technologies
Ethical and Social Implications
- Impact of automation on the workforce
- Ensuring equity, bias mitigation, and fairness
- Data privacy and security safeguards
- The evolving framework of AI ethics
Practical Laboratory Exercises
- Programming assignments utilizing Python
- Analysis of authentic datasets
- Construction and evaluation of basic machine learning models
Conclusions and Recommended Pathways
Requirements
- A foundational grasp of basic programming concepts
- Demonstrated experience with Python programming
- Familiarity with fundamental statistical 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.
