Training: Fundamentals of Artificial Intelligence for Engineers and Technicians
This training offers a practical introduction to the fundamentals of Artificial Intelligence, specifically designed for engineers and technicians. It covers the basics of Machine Learning, focusing on the key mathematical and algorithmic concepts essential to understanding it. Through case studies, participants will discover Machine Learning application scenarios and learn how to select the right tools for their technical projects.
The training also explores the main Machine Learning models—both supervised and unsupervised—as well as the use of PyCaret, an automated Machine Learning tool, including a step-by-step guide for installation and data integration. A dedicated module on Deep Learning introduces neural networks and their applications in engineering contexts.
Finally, engineers and technicians will gain an in-depth understanding of Large Language Models (LLMs), exploring their mechanisms and use cases across various technical projects.
This comprehensive program enables participants to acquire strong technical skills to apply Artificial Intelligence within their specific fields.
Fundamentals for Understanding Artificial Intelligence
- Introduction to key AI concepts: algorithms, ML, DL, and LLM.
- Exploration of algorithms integrated into ML, DL, and LLM.
- The importance of mastering Machine Learning before tackling Deep Learning and LLMs.
Module 1: Overview of Main Deep Learning Models
- Introduction to core Deep Learning concepts and neural networks.
- Overview of Deep Learning applications and limitations.
Module 2: Overview of Main Large Language Models (LLMs)
- How LLMs work and understanding their applications.
- Exploration of the limits and challenges of LLMs.
Module 3: Introduction to Machine Learning
- Fundamental concepts of Machine Learning.
- Overview of commonly used ML algorithms.
- Why ML is a key step before DL and LLMs.
Module 4: Machine Learning Models
- Supervised learning: theoretical principles and practical examples.
- Unsupervised learning: methods and use cases.
Module 5: Using PyCaret for Machine Learning
- Installing and configuring PyCaret.
- Exploring the models available in PyCaret.
- Handling datasets for Machine Learning tasks.
Next Step After This Training
OpenAI Training for Developers
Autre(s) cours dans cette catégorie
Préalables :
Objectifs :
- Understand the fundamental concepts of Machine Learning and their technical applications.
- Master the use of PyCaret to automate Machine Learning workflows.
- Explore neural network architectures and their role in Deep Learning.
- Acquire practical knowledge of Large Language Models (LLMs) and their technical use cases.
En ligne
- 20/11/2025
- 04/12/2025
- 02/01/2026
- 16/01/2026
- 13/02/2026
- 27/02/2026
- 27/03/2026
- 10/04/2026
- 08/05/2026
Montréal
- 18/12/2025
- 13/03/2026
Québec
- 30/01/2026
- 24/04/2026
Pricing
Practical information
- Duration: 1 day(s)
- Schedule: 9:00 a.m. to 4:30 p.m. (2 coffee breaks + 1-hour lunch)
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Format:
- Online (live virtual classroom)
- Or in person, depending on availability
📄 Download course outline (PDF)
Registration detailsInterested in this training?
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Registration with credit card payment
“ I want to thank you both for providing my resources some excellent training(Cobol) over the past 3 days. Mamadou, thank you for being so accommodating on such short notice and for sending your facilitator to Gatineau for this customised and personalised training course. We’ll look forward to continuing our partnership for future training needs. “
“J’ai grandement apprécié les méthodes d’enseignement du prof. Le fait que nous soyons un petit groupe a grandement facilité les apprentissages. Il s’adapte à son audience et les exercices sont formateurs. Je recommande fortement. “
“ Ce fut un plaisir de faire affaires avec Doussou Formation. Ce qui fait LA différence est le service personnalisé totalement à l'écoute des participants ainsi que l'adaptation aux besoins de formation. Flexibilité / Adaptabilité / Professionnalisme / Courtoisie. Merci!“