Course Introduction

The "Artificial Intelligence for Professionals" training course provides a comprehensive overview of the practical experiences from organizations that have successfully implemented Artificial Intelligence (AI), data analytics, and data science. AI has permeated all aspects of personal and professional life, and as its usage grows, it becomes increasingly crucial for individuals to understand how to effectively integrate AI within their work environment.

AI delivers impressive results with minimal effort and cost, making it essential to learn how to manage business operations wisely and deliver optimized, efficient outcomes. This course will cover the key concepts of AI, demonstrating how and when to apply them, and helping participants understand AI theories and how to address real-world challenges using AI.

Through this training, delegates will gain practical knowledge on how to utilize AI tools and techniques effectively, ensuring value for money while cultivating a data-driven, scientific approach to management. The course is designed to equip professionals with the skills needed to implement modern AI methods and foster a culture of data analysis.

The "Artificial Intelligence for Professionals" course will focus on:

• The critical role of leadership in AI and analytics success.
• A systematic approach to understanding data within an organization.
• The active involvement of leaders in AI and analytics efforts.
• How leaders can drive organizational transformation to adapt to AI.
• Leading organizational changes in people, data, systems, culture, and structure.

Course Details

Objectives

This course aims to:

• Provide a clear understanding of how to integrate AI and data analytics into business operations.
• Equip participants with the skills to solve real-world problems using AI.
• Teach how to manage AI projects effectively and optimize AI implementation in business.

Training Methodology

The course combines practical examples, theoretical insights, and case studies, offering a hands-on approach to understanding AI’s role in business.

Organizational Impact

By completing this course, participants will be able to lead AI-driven changes within their organizations, promoting a data-informed, agile approach to decision-making.

Personal Impact

Professionals will develop a deep understanding of AI tools and techniques, enhancing their ability to contribute strategically to AI projects and data analysis efforts.

Who Should Attend

This course is ideal for professionals, including business leaders, data scientists, analysts, and managers, looking to leverage AI and data analytics to drive organizational success.

Daily Agenda

DAY 1: Artificial Intelligence Essentials

• The role of AI in successful leadership
• How AI adds value to business and organizations
• Discovering patterns in data through analysis
• Implementing AI for successful data analysis
• Planning and managing AI projects effectively

DAY 2: Artificial Intelligence and Data Analysis

• Identifying business opportunities for AI integration
• Data architecture for AI implementation
• Setting business priorities in dynamic environments
• Monitoring for anomalies in data
• Improving data quality through AI solutions
• Embracing diverse perspectives in problem-solving

DAY 3: Capabilities Required for Artificial Intelligence

• Effective use of AI tools like ChatGPT
• Exploring fundamental domain opportunities
• Evaluating ROI and prioritization despite incomplete data
• Understanding supervised and unsupervised learning
• Overview of classification and clustering methods
• Introduction to artificial neural networks

DAY 4: Technology Roadmaps for Artificial Intelligence Use

• Understanding fuzzy sets and fuzzy logic
• Practical applications of fuzzy logic in AI
• Real-world examples of fuzzy controllers
• Developing deep domain knowledge
• Rapid application of initial AI solutions
• Focus on continuous development

DAY 5: Structure, Process, and Incentive Changes with AI

• Problem-driven, upfront analytics planning
• Analyzing regressions, likelihoods, and distributions
• Extracting data insights
• Understanding and calculating Root Mean Square Error (RMSE)
• Using Area Under the Curve (AUC) for model evaluation
• Future strategies for digital transformation and AI integration