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Course Outline
Introduction to AI
- History of AI.
- Definitions and terminology.
- AI versus 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
- A solid understanding of basic programming concepts.
- Experience with Python programming.
- Familiarity with basic statistics and mathematics.
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.
