Objectives

Images are a source of multiple information and can be used to respond to many business problems.

This Python Artificial Intelligence course will allow you to perform data analyses using machine learning. You will learn how to transform an image and extract information from it.

At the end of the training, you will know:

What are the key steps in creating a Computer Vision model
How to apply each step
Generate training data by avoiding bias

5

maximum number of people

3 days

of training courses

French

as the main language

Prerequisites

Python language practice.

Target audience

Python developers who want to master the main machine learning and image processing devices.

Educational resources

The training alternates between theoretical and practical contributions. In order to bring out as many questions as possible, the final implementation includes simplified development sessions, supervised by a data scientist who is an expert in the subject.

Instructor profiles

Optimiser la façon d’utiliser les ressources pour créer le plus de valeur possible.

Training program

Introduction and reminder

Introduction to Data Science and Machine Learning
Reminder: Python, the basics
Discovery of labeled image libraries

The fundamentals of Computer Vision

Labelled image libraries
- Biases to avoid
- Training data generation
The main steps in creating a Computer Vision model
Discovery of some Python image processing libraries
Presentation of various Computer Vision libraries and technologies

Putting it into practice

Training a Computer Vision algorithm
Evaluating the performance of a Computer Vision algorithm
Feature engineering: pre-processing and data augmentation

Trainings that may be of interest to you

Introduction to Data Science and AI

Our introduction to Data Science & AI gives you the keys to define a data strategy, manage successful projects and calculate the return on investment.

Learn more

AI applied to HR

AI for HR Training: Master People Analytics to leverage your HR data, make data-driven decisions, and boost your employer brand with real-world use cases.