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Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.
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The Training: Mastering Dlib: Facial Recognition and Machine Learning in Python training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.
The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.
To ensure the quality of the Training: Mastering Dlib: Facial Recognition and Machine Learning in Python training, Learni provides the following teaching resources:
For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.
The assessment of skills acquired during the Training: Mastering Dlib: Facial Recognition and Machine Learning in Python training is carried out through:
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General presentation of Dlib, use cases, comparison with OpenCV. Installing Dlib on Windows/Linux/Mac, managing dependencies (Boost, cmake, etc.). Getting started with the Python API. Reading and displaying images, first script examples.
Presentation of Dlib's face detection algorithms (HOG, CNN). Using the default face detector. Extraction and visualization of facial landmarks (keypoints), analysis of face geometry. Practical exercises on images and video.
Creating facial recognition applications. Using Dlib's embedding (dlib.face_recognition_model_v1), generating and comparing face encodings. Structuring face databases. Performance optimization, real-time pipeline management, result evaluation, best practices and limitations. Presentation of concrete projects and supervised mini-project.
Target audience
Developers, engineers, students, or AI professionals wishing to develop facial recognition applications with Dlib
Prerequisites
Basic Python programming skills and fundamental machine learning concepts
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