This training module provides a comprehensive introduction to Artificial Intelligence (AI), covering its definition, historical development, and core types. Participants will explore essential AI concepts such as machine learning, model architectures, and the principles of learning. Real-world case studies demonstrate how AI is revolutionizing industries—from healthcare and finance to manufacturing and logistics.

The course also introduces popular AI tools and frameworks for practical implementation, outlines techniques to evaluate AI models, and addresses critical ethical considerations and bias mitigation strategies.

Duration : 1-Day – Offered Virtually or Face-to-Face

Language : French or English

The training program includes the following topics:

  • 1. Introduction to AI

    This section introduces the fundamental concept of AI—what it is, how it has evolved over time, and the various types, including narrow AI, general AI, and superintelligence. It also outlines major milestones in AI development and explores its growing impact across sectors such as healthcare, finance, manufacturing, and retail.

  • 2. Key Concepts of AI

    Dive into the core concepts that power AI systems. Learn about machine learning as a subset of AI, the distinction between supervised, unsupervised, and reinforcement learning, and the underlying architectures such as neural networks, decision trees, and support vector machines.

  • 3. Practical Applications of AI

    This section focuses on real-world use cases of AI, showcasing how it is used to solve problems, improve efficiency, and drive innovation. Examples include AI in fraud detection, predictive maintenance, customer service chatbots, personalized recommendations, and autonomous systems.

  • 4. Introduction to AI Implementation

    Explore the key tools and platforms used to build AI solutions. Get familiar with popular frameworks such as TensorFlow, PyTorch, Scikit-learn, and cloud-based services like AWS AI, Google AI, and Azure ML. Understand the basics of how these tools support data processing, model training, and deployment.

  • 5. Evaluation of AI Models

    Learn how to assess the performance and reliability of AI models using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. This section also covers validation techniques like cross-validation and the importance of avoiding overfitting.

  • 6. AI Ethics and Bias

    Understand the ethical considerations in developing and deploying AI systems. Topics include algorithmic bias, fairness, transparency, accountability, and the social implications of AI-driven decisions. This section emphasizes the importance of building responsible and inclusive AI.