Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Artificial Intelligence (AI), Machine Learning (ML) and Data Science
- Al in a historical setting and combinatorial technologies
- Introduction to Al, concepts, narrow and general Al o Different types of Al
- Al - sense, reason, act
- The thinking in Al: Machine learning
- Advanced Analytics vs Artificial Intelligence
- Looking back, now, forward
- 4 types of data analytics
- Analytics value chain
- Algorithms but without technical jargon
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Data as fuel for Al
- Structured and unstructured data o The 5 V's of data
- Data governance
- The data engineering platform
- Just enough to understand the data architecture
- Big data reference architecture
- 3 categories of data usage
Al opportunity matrix
Successful use cases by Porter's value chain
- Primary activities
- Supporting activities
Successful use cases by technology
- NLP
- Image recognition
- Machine learning
Ideation of Al projects
- Al Funnel process
- Several idea generation approaches
- Prioritize projects
- Al project canvas
Running of Al projects
- Machine learning life cycle
- Al machine learning canvas
- When to make and when to buy Al solutions
How to transform to an Al-ready organization
- Use the Al strategy cycle
- Dimensions of the Al framework
- Practical approach to assess the Al maturity of the organization
- Best organizational structures
- Benefits of an Al Center of Excellence
- Skills and competencies
Al and ethics
- Risks of Al
- Ethical guidelines
- Realizing trustworthy AI
35 Hours