Course Code: aiextensivecourse
Duration: 84 hours
Prerequisites:

Requirements

  • Familiarity with programming
  • Basic understanding of algorithms

Audience

  • Developers
  • Senior, middle and high potential management
  • Any professional interested in AI
Overview:

Artificial Intelligence (AI) is a branch of computer science that focuses on the development of intelligent machines capable of performing tasks that typically require human intelligence and it aims to simulate human-like cognitive processes.

This instructor-led, live training (online or onsite) is aimed at professionals who wish to learn and understand the concept of AI and how to use it effectively and responsibly.

By the end of this training, participants will be able to:

  • Learn the concept of Artificial Intelligence (AI).
  • Understand the limits and dangers of AI and use it responsibly.
  • Know how to effectively use AI in real-world scenarios.
  • Explain AI as a concept and all its applications
  • Apply the different AI applications in the business value chain
  • Demonstrate the technologies and algorithms behind AI
  • Apply best practices in an AI project with its activities
  • Assess the available and necessary skills and competencies
  • Discuss on a qualifi ed level with business and data specialists on relevant topics
  • Create and execute an AI strategy and develop an AI ready organization
Course Outline:

Introduction

  • Definition and scope of Artificial Intelligence (AI)
  • Historical and key milestones

Ethical Considerations and Future Trends in AI

  • Ethical challenges in AI development and deployment
  • Bias and fairness in AI algorithms
  • Explainable AI and interpretability
  • Future trends and advancements in AI research

Overview of the Uses of AI

  • Problem-solving using AI techniques
  • Machine learning and its applications
  • Basics of artificial neural networks
  • Deep learning
  • Natural Language Processing (NLP)
  • Computer vision
  • Robotics
  • AI in healthcare
  • AI in finance
  • Effective uses and impact of AI

Privacy Protection and Compliant use of AI

  • Importance of data privacy and protection
  • in AI applications
  • Laws and regulations related to data privacy
  • Importance of transparency and explainability in AI systems
  • Consent and user rights
  • Security risks and vulnerabilities in AI applications
  • Overview of regulatory frameworks governing AI
  • Compliance requirements for AI systems in specific industries
  • Impact of AI regulations on privacy protection and compliant use
  • Best practices for ensuring compliant use of AI and privacy protection

Overview of Artificial Intelligence (AI)

  • Machine learning systems

Exploring Applications for AI

  • AI in the corporate context

Learning About the Technology of AI

  • Underfit and overfit, classification, and regularization
  • Multi-layer perception (MLP) and deep learning
  • Convolutional and recurrent neural networks

Assessing Strategic Approaches

  • Commissioning or procurement (build or buy?)
  • AI maturity models for your organization

Working With Data in Your Organization

  • Data readiness evaluation
  • Word embeddings
  • Training with artificial data

Assessing AI Project Selection

  • Key criteria for project selection

Managing an AI Project

  • Machine learning versus deep learning
  • Project management (lifecycle, timescales, methodology)
  • Operations, maintenance, and risk management

Gathering Feedback

  • Implementing feedback methods (surveys, interviews, etc.)
  • Key stakeholders who will provide feedback
  • Analyzing results

Introduction to Artificial Intelligence (AI), Machine Learning (ML) and Data Science

  • Al in a historical setting and combinatorial technologies
  • Introduction to Al, concepts, narrow and general Al o Different types of Al
  • Al - sense, reason, act
  • The thinking in Al: Machine learning
  • Advanced Analytics vs Artificial Intelligence
  • Looking back, now, forward
  • 4 types of data analytics
  • Analytics value chain
  • Algorithms but without technical jargon
  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • Data as fuel for Al
  • Structured and unstructured data o The 5 V's of data
  • Data governance
  • The data engineering platform
  • Just enough to understand the data architecture
  • Big data reference architecture
  • 3 categories of data usage

Al opportunity matrix

Successful use cases by Porter's value chain

  • Primary activities
  • Supporting activities

Successful use cases by technology

  • NLP
  • Image recognition
  • Machine learning

Ideation of Al projects

  • Al Funnel process
  • Several idea generation approaches
  • Prioritize projects
  • Al project canvas

Running of Al projects

  • Machine learning life cycle
  • Al machine learning canvas
  • When to make and when to buy Al solutions

How to transform to an Al-ready organization

  • Use the Al strategy cycle
  • Dimensions of the Al framework
  • Practical approach to assess the Al maturity of the organization
  • Best organizational structures
  • Benefits of an Al Center of Excellence
  • Skills and competencies

Al and ethics

  • Risks of Al
  • Ethical guidelines
  • Realizing trustworthy AI

Summary and Wrap up