An Intensive 10-day Training Course
Artificial Intelligence Practices and
Principles for Leaders and Managers
Artificial Intelligence Practices and Principles for Leaders and Managers
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MODULES
This PetroKnowledge 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 two-week training course.
Daily Agenda
Module 1: Artificial Intelligence (AI) for Leaders and Managers
Day One: 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 Two: 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 Three: 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 Leader
Day Four: 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 Five: 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 Six: 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 Seven: 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 Eight: 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 Nine: 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 Ten: Ethical Considerations and Practical Applications
- Bias and fairness in AI
- Ethical guidelines and frameworks
- Responsible AI practices
- 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
Certificate
- On successful completion of this Training Course / Online Training Course, a PetroKnowledge Certificate / E-Certificate will be awarded to the delegates.
