• Define artificial intelligence and its types
• Explain how artificial intelligence senses, reasons, and acts
• Describe machine learning and its categories
• Distinguish between advanced analytics and artificial intelligence
• Identify the algorithms behind machine learning
• Describe the characteristics and challenges of data
• Apply best practices for data governance
• Describe the components and categories of the data engineering platform
• Identify the opportunities and use cases for AI in various industries
• Generate and prioritize ideas for AI projects
• Run AI projects using a systematic approach and framework
• Decide when to make or buy AI solutions
• Transform your organization to be AI-ready
• Identify the skills and competencies required for AI professionals
• Address the ethical issues and risks associated with AI

This course is designed for professionals who want to learn how to understand the concepts, technologies, and applications of artificial intelligence, machine learning, and data science. The course is suitable for professionals from any industry or sector who want to gain a foundational knowledge of AI.

Day One:

Artificial Intelligence in Historical Setting and Combinatorial Technologies

• Introduction to the course: objectives expectations agenda
• What is artificial intelligence? Definition history models
• How does artificial intelligence differ from narrow and general AI? Narrow vs general AI examples challenges
• What are the different types of artificial intelligence? Reactive memory theory of mind self-awareness
• How does artificial intelligence sense reason act? Sensing reasoning acting examples techniques tools
• Self-assessment: How familiar are you with artificial intelligence?

Day Two:

The Thinking in AI: Machine Learning

• What is machine learning? Definition history types purposes
• How does machine learning enable artificial intelligence to learn from data experience? Learning from data experience examples techniques tools
• What is the difference between advanced analytics artificial intelligence? Advanced analytics vs artificial intelligence definitions examples benefits challenges
• What are the four types of data analytics? Descriptive diagnostic predictive prescriptive definitions examples benefits challenges
• Quiz: Test your knowledge on machine learning

Day Three:

Data as Fuel for AI

• What is data? Definition types sources
• Why is data important for artificial intelligence? Benefits challenges examples
• How can you describe the characteristics challenges of data? The 5 V’s of data volume velocity variety veracity value definitions examples challenges solutions
• How can you apply best practices for data governance? Data governance definition principles components examples
• What are the components categories of the data engineering platform? Data engineering platform definition types categories examples
• Case study: Analyze a given data set using various techniques tools

Day Four:

AI Opportunity Matrix

• What are the opportunities use cases for AI in various industries? Opportunities use cases definitions examples benefits challenges
• How can you map the opportunities use cases for AI using Porter’s value chain model? Porter’s value chain model definition components examples
• What are some of the successful examples of AI applications using various technologies? Natural language processing image recognition machine learning etc. definitions examples benefits challenges
• How can you generate prioritize ideas for AI projects using various approaches tools? AI funnel process idea generation approaches prioritization tools AI project canvas
• Exercise: Generate prioritize ideas for AI projects using various approaches tools

Day Five:

Running of AI Projects

• What is a systematic approach framework for running AI projects? Systematic approach framework definition steps components examples
• How can you use the machine learning life cycle to run AI projects? Machine learning life cycle definition stages components examples
• How can you use the AI machine learning canvas to run AI projects? AI machine learning canvas definition elements components examples
• How can you decide when to make or buy AI solutions? Make or buy decision definition advantages disadvantages criteria examples
• Feedback: Evaluate your AI project skills based on a given scenario

To enhance learning and practical application of concepts, the training course will use a combination of interactive lectures, case studies, group discussions, practical exercises, and real-world examples. Participants will also get the chance to collaborate on group projects and create action plans adapted to the needs of their respective organizations.

Outline: The course will run for five days with each day covering a different topic. The course will consist of lectures discussions exercises case studies videos quizzes feedback sessions