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Training on Artificial Intelligence for Professionals

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About Course

As AI technologies mature, professionals across industries must understand not only the underlying algorithms but also how to integrate AI into strategic initiatives, optimize workflows, and manage risks. This course blends strategic insight, technical know-how, and practical frameworks to empower participants to lead and execute AI projects effectively within their organizations.
Participants will gain the tools to evaluate AI opportunities, formulate strategy, liaise with data science teams, and ensure responsible deployment.

Target Participants

This program is tailored for mid- to senior-level professionals, including managers, team leaders, business analysts, and technical specialists, who are responsible for planning, overseeing, or collaborating on AI initiatives.

What You Will Learn

By the end of this course, the participants will be able to:

  • Identify the best analytics strategy for the organization
  • Learn the critical time and opportunity for change with the use of AI
  • Understand the business value of the data analysis and AI implementation
  • Implement analysis and scientific decision-making, and embed the AI tools in your business
  • Adopt the four stages of analytics in an organization

Course Duration

  • Classroom-Based: 5 Days
  • Online: 7 Days

What Will You Learn?

  • Identify the best analytics strategy for the organization
  • Learn the critical time and opportunity for change with the use of AI
  • Understand the business value of the data analysis and AI implementation
  • Implement analysis and scientific decision making, and embed the AI tools in your business
  • Adopt the four stages of analytics in organization

Course Content

Artificial Intelligence Essentials
Data analysis and AI for successful leadership AI added value to business and organization Discovering patterns in data using analysis Successful data analysis and artificial intelligence implementation Planning and managing AI projects

Artificial Intelligence and Data Analysis
The business context of opportunities Data architecture for the implementation of artificial intelligence Business priorities in dynamic and challenging business environments Monitoring for anomalies in data Improving data quality through the use of AI Respecting diverse perspectives of problem solving

Capabilities Required for Artificial Intelligence
Effective use of Chat GPT Fundamental domain opportunities ROI for prioritization, despite missing data Supervised and unsupervised learning Classification and clustering Artificial neural networks

Technology Roadmaps for Artificial Intelligence Use
Fuzzy set and Fuzzy rules Importance of Fuzzy logic Real example of Fuzzy controllers Deep domain understanding Applying initial solutions rapidly Development focus

Structure, Process, and Incentive Changes with AI
Problem-driven and planned up-front analytics Regressions, likelihood, distributions Data insights Root Mean Square Error (RMSE) Area Under the Curve (AUC) Future strategies and digital transformation

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