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Modules
This training course is split into the following modules:
Module I - Artificial Intelligence (AI) for Leaders and Managers
Module II - Principles and Practices of Artificial Intelligence (AI)
Course Outline
Module I: Artificial Intelligence (AI) for Leaders and Managers
Day 1
Unlocking AI’s Power – Transforming Business for the Future
- Exploring Cutting-Edge AI Technologies and Innovations
- How AI Fuels Disruption and Competitive Advantage
- Global AI Adoption Trends Shaping Industries
- AI’s Value Proposition: Turning Data into Business Insights
- Defining Success: Key Metrics for AI-Driven Growth
Day 2
Mastering AI – Tools and Strategies for Professionals
- Demystifying Machine Learning and Deep Learning
- Navigating the AI Ecosystem: Essential Tools and Platforms
- AI in Action: Enhancing Productivity and Problem-Solving
- Data-Driven Strategies for Smarter Decision-Making
- Real-World Success Stories: AI Transformations Across Industries
Day 3
AI-Powered Leadership – Driving Smart Decisions
- Building Intelligent Decision-Making Frameworks
- Leadership in the AI Era: Strategies for Seamless Adoption
- Managing AI-Driven Projects Across Teams and Functions
- Ethics, Compliance, and Trust in AI Implementation
- Leading with AI: Lessons from Successful Industry Leaders
Day 4
AI Risk & Governance – Balancing Innovation with Responsibility
- Identifying and Controlling AI-Related Risks
- Tackling AI Bias, Fairness, and Transparency Challenges
- Crafting Robust AI Governance and Compliance Policies
- Aligning AI Strategies with Long-Term Business Vision
- Tools for Ensuring AI Integrity and Performance Monitoring
Day 5
The AI-Driven Future – Scaling Innovation and Impact
- Building a Culture of AI-First Thinking and Innovation
- Bridging the Gap: Collaboration Between AI Experts and Teams
- Scaling AI Solutions for Enterprise-Wide Impact
- Measuring AI ROI: Proving Value and Driving Continuous Growth
- The Next Frontier: Emerging Trends Shaping the Future of AI
Module II: Principles and Practices of Artificial Intelligence (AI)
Day 6
Introduction to AI Fundamentals
- Definition of AI
- Historical overview
- AI applications across industries
- Basic concepts of machine learning
- Supervised, unsupervised, and reinforcement learning
- Examples of machine learning applications
- Basics of Python programming language
- Introduction to libraries such as NumPy, Pandas, and Matplotlib for data manipulation and visualization
Day 7
Machine Learning Algorithms
- Theory behind linear regression
- Implementation of linear regression for prediction tasks
- Logistic regression for classification tasks
- Introduction to decision trees
- Ensemble methods: Random Forests
- Practical examples and applications
- Hands-on exercises implementing linear regression, logistic regression, decision trees, and random forests using Python libraries
Day 8
Neural Networks and Deep Learning
- Basics of neural networks architecture
- Activation functions, layers, and optimization algorithms
- Feedforward and backpropagation algorithms
- Convolutional Neural Networks (CNNs) for image recognition
- Recurrent Neural Networks (RNNs) for sequential data
- Transfer learning and pre-trained models
- Building and training neural networks for image classification and sequence prediction tasks using TensorFlow or PyTorch
Day 9
Advanced Topics in AI
- Introduction to reinforcement learning concepts
- Q-learning, policy gradients, and deep reinforcement learning
- Applications of reinforcement learning in robotics, gaming, and autonomous systems
- Basics of NLP techniques
- Text preprocessing, tokenization, and feature extraction
- Applications of NLP in sentiment analysis, language translation, and chatbots
- Implementing reinforcement learning algorithms and NLP techniques on practical examples
Day 10
Ethical Considerations and Practical Applications
- Bias and fairness in AI
- Ethical guidelines and frameworks
- Responsible AI practices
- Case studies and examples of AI implementation in various industries
- Challenges and opportunities in deploying AI solutions
- Participants present their capstone projects, showcasing their understanding and application of AI principles and techniques
- Open discussion and feedback session
Certificates
- Upon successful completion of the classroom-based training course, GLOMACS Certificate will be awarded to the delegates. Continuing Professional Education credits (CPE): In accordance with the standards of the National Registry of CPE Sponsors, one CPE credit is granted per 50 minutes of attendance
- Upon successful completion of the online training course, a GLOMACS Certificate will be awarded to all delegates. Guided Learning Hours – In accordance with ISO 9001:2015–certified quality management standards, one Guided Learning Hour is awarded for every 60 minutes of attendance.
Providers and Associations
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