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Course Outline
Introduction to AI Engineering
- What is AI engineering?
- The evolution of AI and its impact on engineering
- Key concepts and terminology in AI
Core AI Technologies
- Understanding machine learning
- Deep learning and neural networks
- Natural language processing (NLP)
AI Problem Solving
- Identifying problems suitable for AI solutions
- Data collection and preprocessing
- Model selection and training
AI in Software Development
- AI tools for developers
- Integrating AI into existing systems
- Version control and model management
AI and Data Engineering
- Big data technologies and their role in AI
- Data pipelines and ETL processes
- Data storage and management for AI
Ethical AI
- Understanding bias and fairness in AI systems
- Privacy and security in AI engineering
- Ethical considerations and best practices
AI Project Management
- Agile methodologies for AI projects
- Team roles and responsibilities
- Documentation and reporting
Hands-On AI Engineering
- Setting up your AI development environment
- Building and evaluating simple AI models
- Collaborative AI engineering projects
The Future of AI Engineering
- Emerging trends in AI
- Continuous learning and skill development
- Career opportunities in AI engineering
Summary and Next Steps
Requirements
- An understanding of basic programming concepts
- Experience with Python programming
- Familiarity with basic statistics and linear algebra
Audience
- AI engineers
- Software developers
- Data analysts
14 Hours
Testimonials (2)
examples and exercises
Kamil
Course - Introduction to Data Science and AI using Python
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