Courses IA engineer

Description of the training

Intensive training in artificial intelligence, covering the fundamentals of machine learning, deep learning, neural networks (CNN, RNN), generative AI as well as deployment and optimization of IA models in production with tools such as TensorFlow, Keras and PyTorch.

Objectives of training

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

• Master the fundamentals of artificial intelligence, including supervised, unsupervised and deep learning.
• Develop, train and deploy AI models using modern frameworks and tools.
• Implement optimized IA solutions for concrete use cases in various sectors.
• Understand ethical considerations, biases and regulations in AI.
• Being able to integrate IA pipelines into production systems while ensuring their scalability.

Who is this training for?

The training is aimed at a wide audience, including:

• Software developers and engineers: wishing to acquire specialized skills in the development and implementation of artificial intelligence solutions.
• Data Scientists are evolving: seeking to deepen their knowledge of advanced AI and integrate AI pipelines into production environments.
• Cloud Architects and Infrastructure: Wants to understand the implications of AI for designing optimized and scalable architectures.
• IA students and researchers: wanting to transform their theoretical skills into concrete and impacting applications.
• IT Professionals in Conversion: Aspired to integrate the field in high demand of artificial intelligence to boost their careers.

Prerequisites

No specific prerequisites are required.

Training programme

Module 1 : Fondamentaux de l'IA et du Machine Learning (Jours 1 à 4)

• Objective: To understand the basics and principles of learning algorithms.
• Content:
  Key concepts of AI and differences with Data Science.
  Types of learning: supervised, unsupervised, semi-supervised and reinforced.
  Introduction to Python for AI.
  Data handling with libraries like NumPy and Pandas.

Module 2 : Modèles d'Apprentissage Supervisé et Non Supervisé (Jours 5 à 7)

• Objective: Develop high-performance models using various algorithms.
• Content:
  Linear and logistical regressions.
  Decision trees, random forests and boosting.
  Clustering (K-Means, DBSCAN) and dimension reduction (ACP).
  Model evaluation and validation (metric and cross-validation).

Module 3: Introduction to Deep Learning (Days 8-10)

• Objective: To understand the basics of neuron networks and build deep models.
• Content:
  Operation of neural networks and propagation.
  Popular Frameworks: TensorFlow and PyTorch.
  Convolutive Networks (CNN) for image analysis.
  Recurrent Networks (RNN) for Time Series and Natural Language Processing (NLP).

Module 4: Deployment of Models and MLOps (Days 11-13)

• Objective: To automate and manage the life cycle of AI models in production.
• Content:
  Production environments for AI.
  Introduction to automated MLOps and pipelines.
  Integration with tools such as Docker, Kubernetes and CI/CD.
  Monitoring and maintenance of deployed models.

Module 5: Practical Projects (Days 14 to 16)

• Objective: To apply the acquired knowledge to real problems.
• Content:
  Case Study 1: Prediction of the Application.
  Case Study 2: Image Classification.
  Case Study 3: Sensitive Analysis with NLP.

Module 6 : Éthique, Réglementation et Futur de l’IA (Jours 17 à 18)

• Objective: To explore the social and regulatory aspects of AI.
• Content:
  Bias and discrimination in IA models.
  Local and international regulations in AI.
  Emerging trends: IA generative, IA ethical, IA green.
  Presentation of final projects and feedback.

Training assets

• Pedagogical and modular approach: Alternative between theory and practice for better assimilation of concepts.
• Cloud Integration: Strong focus on cloud and distributed solutions.
• Qualified speakers: Specialist trainers with practical experience in the field.
• Educational tools and materials: Access to online resources, live demonstrations and real-life case studies.
• Accessibility: Training is open to all, without advanced technical prerequisites.
• Implementation: Complete project from the end of the modules to consolidate the achievements.
• Preparation for Industry: Focus on standard certifications and tools used in the professional environment.

Pedagogical methods and tools used

• Live demonstrations with data science services.
• Practical workshops and real case studies in various sectors (industry, trade, health).
• Feedback: Sharing best practices and common mistakes in business.
• Simulations and tools: Using simulators for interactive workshops.

Evaluation

• End of training QCM to test the understanding of the concepts addressed.
• Practical case studies or group discussions to apply the knowledge gained.
• Ongoing evaluation during practical sessions.
• Implementation: Complete project from the end of the modules to consolidate the achievements.

Normative References

• Well-Architected Cloud Framework.
• GDPR (General Data Protection Regulation).
• ISO 27001, SOC 2 (Service Organization Control).
• NIST Cybersecurity Framework.