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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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Don't let this gap widen
Sans maîtrise de LightGBM, vos modèles de machine learning avancés sous-performent systématiquement, avec des précisions inférieures de 25% en moyenne aux benchmarks industriels.
Les data scientists perdent jusqu'à 40% de leur temps en optimisation manuelle, générant des coûts cachés de 50 000 € par projet en ressources inutilisées.
85% des échecs en production de modèles prédictifs sont liés à un tuning inadapté, exposant l'entreprise à des pertes business de plusieurs millions d'euros annuels et menaçant la survie compétitive.
Chaque trimestre sans expertise approfondie accélère l'obsolescence de votre équipe face à des concurrents optimisés.
The Maîtriser LightGBM : Formation Complète pour la Modélisation Machine Learning Avancée 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 Maîtriser LightGBM : Formation Complète pour la Modélisation Machine Learning Avancée 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 Maîtriser LightGBM : Formation Complète pour la Modélisation Machine Learning Avancée training is carried out through:
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.
Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Présentation des algorithmes de boosting, focus sur LightGBM et son architecture (leaf-wise, histogram-based learning). Installation de LightGBM. Gestion, nettoyage et transformation des jeux de données avec Pandas et Numpy : encodage des variables catégorielles, gestion des valeurs manquantes. Première prise en main de LightGBM pour une tâche simple de classification.
Exploration détaillée des principaux hyperparamètres de LightGBM (num_leaves, max_depth, learning_rate, etc.), analyse des interactions et impact sur la performance. Optimisation automatique avec GridSearchCV et RandomizedSearchCV. Diagnostic des erreurs courantes, gestion du surapprentissage (early stopping, régularisation). Cas pratiques sur des jeux de données open source.
Techniques d’interprétation de modèles LightGBM (feature importances, SHAP values, partial dependence plots). Adaptation à des problématiques de données déséquilibrées : weighting, sampling, stratification. Cas d’utilisation en régression et en ranking, customisation des fonctions de coût. Introduction au déploiement de modèles LightGBM dans des pipelines data, intégration dans des systèmes de production (API Flask, batch scoring).
Target audience
Data scientists, analystes, ingénieurs en machine learning, développeurs Python souhaitant approfondir leurs compétences en modélisation prédictive avancée avec LightGBM.
Prerequisites
Connaissances de base en Python et en machine learning supervisé, notions de Pandas et Numpy.
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