Objectives of the training
The objective of this course is to enable participants to understand the fundamentals of AI, work effectively with data, and develop Machine Learning and Deep Learning models to address real-world problems.Targeted audience
Data scientists, data analysts, developers wishing to specialize in AI, software engineers, technical architects, and data-oriented technical project managers.Prerequisite
Basic knowledge of Python, concepts in statistics and linear algebra, as well as familiarity with data science concepts.Trainers
Benefits for Participants
• Understand the foundations of AI and its role in Data Science.
• Manipulate and prepare data for AI models.
• Implement Machine Learning and Deep Learning algorithms.
• Use AI frameworks (TensorFlow, PyTorch, Scikit‑learn).
• Deploy a complete AI project, from design to evaluation.
Course architecture
Day 1: Introduction to AI and Data Science
Chapter 1: Fundamental concepts
• Definition of AI, Machine Learning, Deep Learning.
• Applications in Data Science.
Chapter 2: Ecosystem and tools
• Python for AI.
• Key libraries: NumPy, Pandas, Matplotlib.
• Practical workshop:
• Exploration and visualization of a dataset.
Day 2: Data preparation and processing
Chapter 3: Data collection and cleaning
• Handling missing data.
• Normalization and encoding.
Chapter 4: Feature Engineering
• Variable selection and transformation.
• Practical workshop:
• Preparing a dataset for a predictive model.
Day 3: Machine Learning algorithms
Chapter 5: Supervised learning
• Linear and logistic regression.
• Decision trees and Random Forest.
Chapter 6: Unsupervised learning
• Clustering (K‑means).
• Dimensionality reduction (PCA).
• Practical workshop:
• Implementation of an ML model with Scikit‑learn.
Day 4: Deep Learning and neural networks
Chapter 7: Foundations of Deep Learning
• Neural network architecture.
• Activation functions and backpropagation.
Chapter 8: Advanced frameworks
• TensorFlow vs PyTorch.
• Creation of a simple network.
• Practical workshop:
• Development of an image classification model.
Day 5: Deployment and practical cases
Chapter 9: Evaluation and optimization
• Performance metrics.
• Hyperparameters and tuning.
Chapter 10: Deploying an AI project
• Integration into a real‑world environment.
• Deployment tools (Flask, FastAPI).
• Practical workshop:
• Deployment of an AI model via an API.
Pedagogical details
Type of training
Private or personalized training
Do you have several employees interested in the same training course? Whether in person at your offices or remotely in virtual mode, we offer private training courses tailored to your team's needs. Group rates are available. Contact us for more details or request a quote online.
Request a quotePrivate or personalized training
Do you have several employees interested in the same training course? Whether in person at your offices or remotely in virtual mode, we offer private training courses tailored to your team's needs. Group rates are available. Contact us for more details or request a quote online.
Request a quote