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Course Outline
Introduction to Artificial Intelligence (AI), Machine Learning (ML), and Data Science
- Historical evolution of AI and the integration of combinatorial technologies
- Fundamentals of AI, core concepts, and the distinction between narrow and general AI; classification of AI types
- Core AI functions: sensing, reasoning, and action
- Cognitive processes in AI: an overview of machine learning
- Differentiating advanced analytics from artificial intelligence
- Historical context, current status, and future projections
- Classification of the four types of data analytics
- The analytics value chain framework
- Explanation of algorithms in plain language, avoiding excessive technical jargon
- Principles of supervised learning
- Principles of unsupervised learning
- Fundamentals of reinforcement learning
- The role of data as essential input for AI systems
- Structured versus unstructured data; the 5 V’s of data characteristics
- Frameworks for data governance
- Components of a data engineering platform
- Essential knowledge for understanding data architecture
- Reference architectures for big data systems
- Categorization of data usage patterns into three distinct groups
AI Opportunity Matrix
Proven use cases mapped to Porter’s value chain
- Application within primary activities
- Application within supporting activities
Proven use cases categorized by technology
- Natural Language Processing (NLP)
- Image recognition systems
- Machine learning applications
Conceptualization and Ideation of AI Projects
- The AI Funnel methodology
- Various approaches to generating project ideas
- Strategies for prioritizing AI initiatives
- Utilization of the AI project canvas
Execution and Management of AI Projects
- The machine learning lifecycle
- Application of the AI machine learning canvas
- Decision-making frameworks for developing in-house versus procuring external AI solutions
Strategies for Transforming into an AI-Ready Organization
- Implementation of the AI strategy cycle
- Key dimensions of the AI governance framework
- Practical methods for assessing organizational AI maturity
- Optimization of organizational structures
- Advantages of establishing an AI Center of Excellence
- Identification of required skills and competencies
AI and Ethics
- Identification of risks associated with AI
- Development of ethical guidelines
- Strategies for realizing trustworthy AI systems
35 Hours
Testimonials (4)
The quizzes
Qeeka Chopho - Revenue Services Lesotho
Course - Artificial Intelligence (AI) Strategy for Business and Professionals
The continuation from 1st day to the last. There was a clear synergy of content presented
Mabea Rampeta - Revenue Services Lesotho
Course - Artificial Intelligence (AI) Strategy for Business and Professionals
method of delivery. Not just listening, being hands on and being practical
Makhauhelo Hlaele - Revenue Services Lesotho
Course - Artificial Intelligence (AI) Strategy for Business and Professionals
The practical exercises using the organization as a use case. The style of using the whiteboard also helped in showing the buildup of the ai strategy from the beginning to the end, ensuring that feedback from all the participants is at one place.