Artificial Intelligence Strategy for Professionals
Methodology
This course is designed for senior and middle management who recognize the inevitable need for digital transformation. It is intended for those who understand that innovation and development are part of business practice and want to be prepared to harness the benefits of artificial intelligence (AI). Familiarity with core technology concepts such as data and cloud programming is also recommended.
In summary, this course is tailored for managers who want to understand what AI can do for them, lead digital transformation and data initiatives, rather than comprehending the technical methodologies underneath.
Course Objectives
By the end of the course, participants will be able to:
• Explain the concept of artificial intelligence and its various forms.
• Apply different forms of artificial intelligence in the value chain.
• Present the technologies and algorithms behind artificial intelligence.
• Apply best practices in artificial intelligence projects, including associated activities.
• Evaluate available and required skills and competencies.
• Engage in qualified discussions with business and data specialists on relevant topics.
Target Audience
This course is designed for senior and middle management who recognize the inevitable need for digital transformation. It is intended for those who understand that innovation and development are part of business practice and want to be prepared to harness the benefits of artificial intelligence (AI). Familiarity with core technology concepts such as data and cloud programming is also recommended.
In summary, this course is tailored for managers who want to understand what AI can do for them, lead digital transformation and data initiatives, rather than comprehending the technical methodologies underneath.
Training Program Content
• Introduction to Artificial Intelligence (AI), Machine Learning (ML), and Data Science.
• The concept of artificial intelligence and its forms.
• Artificial intelligence as a blend of modern technologies.
• Artificial intelligence from a historical perspective.
• Artificial intelligence: logic, reasoning, action.
• Thinking in artificial intelligence: machine learning.
• The nine building blocks.
• Algorithms and search engines.
• Supervised learning applications.
• Classification: algorithms like Naïve Bayes.
• Regression: algorithms like linear regression and decision trees.
• Semi-supervised learning applications.
• Algorithms like Q-Learning, SARSA.
• Unsupervised learning applications.
• Clustering: algorithms like k-means and hierarchical clustering.
• Designing an artificial intelligence methodology: collaborative work.
• Practice with the building blocks and use cases.
• Real-world application to establish a private enterprise.
• Creative garage methodology for defining and identifying AI projects.
• AI opportunity matrix.
• Successful use cases by the value chain (Porter).
• Key activities: inbound operations, marketing, sales, and outbound services.
• Support activities: management, finance, human resources, research and development, procurement.
• Successful use cases through technology.
• Natural Language Processing (NLP).
• Image recognition.
• Machine learning.
• Running successful AI projects.
• Project process.
• Idea formation and problem definition.
• Exploratory data analysis.
• Model development.
• Implementation.
• Skills and capabilities.
• Organizational changes.
• Top ten common mistakes.
• AI tools and roadmap.
• Technologies: R, Python, Spotfire, Hadoop.
• Platforms: MS Azure, IBM Watson, Google TensorFlow.
• Developing a roadmap.
• Creating the initial roadmap.
• Develop your strategy and tactics to achieve AI project suppression (AI Funnel).
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