Artificial Intelligence (AI) Practices and Principles for Leaders and Managers Training Course

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Course Description

MODULES

This training course is split into two modules:

MODULE I - Artificial Intelligence (AI) for Leaders and Managers

MODULE II - Principles and Practices of Artificial Intelligence (AI)

Each module is structured and can be taken as a stand-alone training course; however, delegates will maximise their benefits by taking Module 1 and 2 back-to-back as a 2-week training course.

Course Outline

Module 1: 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 2: 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

On successful completion of this 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.

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An Interactive 10-Day Training Course

Artificial Intelligence (AI) Practices and Principles for Leaders and Managers