Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Maîtriser le Machine Learning avec Scikit-learn : Fondamentaux et Pratiques training are: Maîtrise de base du langage Python, notions fondamentales de statistiques. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Maîtriser le Machine Learning avec Scikit-learn : Fondamentaux et Pratiques training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Maîtriser le Machine Learning avec Scikit-learn ... | Learni

The story of Learni

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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Methods, approaches, and teaching resources

The Maîtriser le Machine Learning avec Scikit-learn : Fondamentaux et Pratiques 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.

Teaching resources provided

To ensure the quality of the Maîtriser le Machine Learning avec Scikit-learn : Fondamentaux et Pratiques training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and 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.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Maîtriser le Machine Learning avec Scikit-learn : Fondamentaux et Pratiques training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training objectives

Découvrir l’écosystème Scikit-learn et ses principales fonctionnalités

Conduire des analyses de données préparatoires et exploratoires

Mettre en place des pipelines de machine learning supervisé et non supervisé

Comprendre et appliquer les méthodes d’évaluation et de validation de modèles

Optimiser et interpréter des résultats de différents algorithmes standards du machine learning

Training program

Module 1Fondamentaux de Scikit-learn et workflow machine learning

Introduction au machine learning, installation et écosystème Scikit-learn, appréhension des datasets, exploration des API, structuration du workflow de modélisation, split train/test. Ateliers : exploration de jeux de données réels, compréhension des pipelines Scikit-learn.

Module 2Modélisation supervisée : classification et régression

Présentation des algorithmes de classification (régression logistique, SVM, forêts aléatoires), régression linéaire/ridge/lasso, entraînement de modèles, gestion des features, transformation de données, cross-validation, métriques d’évaluation. Ateliers : classification de chiffres manuscrits, prédiction de prix de logements.

Module 3Apprentissage non supervisé, validation avancée et interprétabilité

Clustering (K-means, Agglomerative), réduction de dimension (PCA), évaluation avancée, tuning hyperparamètres (GridSearchCV), pipelines complexes, et introduction à l’interprétabilité (importance des features, courbes ROC). Ateliers : segmentation de clients, pipeline complet d’optimisation et d’évaluation.