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Course Outline
Introduction to AI
- The history of AI
- Key definitions and terminology
- Comparing AI with human intelligence
- Future trends and potential developments
Machine Learning Fundamentals
- Types of machine learning: supervised, unsupervised, and reinforcement
- Essential ML algorithms
- The ML workflow: from data collection to model evaluation
Data Management
- Techniques for data collection
- Data cleaning and preprocessing methods
- Data analysis and visualization techniques
AI in Practice
- Case studies demonstrating AI applications
- AI solutions tailored to specific industries
- The role of AI in consumer products
Ethical Considerations
- AI and its impact on employment
- Bias and fairness within AI systems
- Privacy and security concerns
- The future landscape of AI ethics
Lab Project
- Python programming assignments
- Data analysis projects utilizing real-world datasets
- Building a simple ML model
Summary and Next Steps
Requirements
- A solid grasp of basic programming concepts
- Proficiency in Python programming
- Knowledge of basic 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.
