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

An Interactive 10-Day Training Course

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

NASBA

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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
  • 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.
Providers and Associations

Endorsed Education Provider

NASBA

In Association With

Options & Brochure
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