Artificial Intelligence Training

Course description

This training offers a complete immersion into the world of artificial intelligence. You will learn to develop and apply AI models using tools such as TensorFlow, PyTorch, and Scikit-learn.

Course objectives

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

• Master key AI concepts: Machine Learning, Deep Learning, and NLP.
• Apply supervised and unsupervised algorithms to real data.
• Develop and deploy AI models with TensorFlow and PyTorch.
• Analyze advanced AI applications in areas such as computer vision and natural language processing.

Who is this course for?

This course is intended for a broad audience, including:

This course is open to:

• Data Analysts: Those wishing to deepen their AI skills.
• Junior Data Scientists: Those looking to advance in AI and machine learning.
• IT Professionals and Developers: Those who want to specialize in data analysis through AI models.
• Project Leads and Managers: Those managing teams working on large-scale data projects.

Prerequisites

Basic knowledge of information systems.

Course curriculum

Introduction to Artificial Intelligence

• Understand the fundamental concepts of AI (Machine Learning, Deep Learning, NLP).
• Get to know the tools used in AI such as Jupyter Notebook and Google Colab.
• Explore supervised algorithms: linear regression, classification, model evaluation.

Unsupervised Learning and Neural Networks

• Introduction to unsupervised learning: clustering, dimensionality reduction with PCA.
• Discover neural networks and implement them with TensorFlow/Keras.
• Implement a simple neural network.

Practical Applications and Advanced Use Cases

• Computer vision applications: Convolutional Neural Networks (CNNs).
• Introduction to Natural Language Processing (NLP).
• Hands-on workshops with real datasets: image analysis, text classification.

Course benefits

• Theoretical and practical training with real-world cases.
• Specialized instructors with solid experience in AI.
• Use of modern tools such as TensorFlow, PyTorch, and Google Colab.

Teaching methods and tools used

• Interactive lectures and live practical demonstrations.
• Real case studies to apply learned concepts and techniques.
• Use of tools like Jupyter Notebook and Google Colab for a complete hands-on experience.

Assessment

• End-of-course multiple-choice quiz.
• Practical case studies.
• Continuous assessment with personalized feedback.

Standards and references

The training follows AI best practices and adheres to data security and management standards.