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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 LightGBM: Advanced Optimization and Deployment for Machine Learning 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 LightGBM: Advanced Optimization and Deployment for Machine Learning 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 LightGBM: Advanced Optimization and Deployment for Machine Learning 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.
Introduction to LightGBM: history, positioning relative to XGBoost, CatBoost, etc. Internal algorithmic workings: Gradient Boosting, leaf-wise trees. Installation on various OS, integration with scikit-learn, hands-on with the Python package. Key differences and production advantages.
Native encoding of categorical variables, handling of missing values. Support for large datasets. Creating tailored datasets, train/test splits, and fast cross-validation techniques. Building robust features and LightGBM-specific feature engineering.
Detailed review of key hyperparameters (num_leaves, max_depth, min_data_in_leaf, learning_rate, early stopping, etc.). Automated tuning methods (GridSearch, RandomSearch, Optuna). Advanced performance metrics: ROC curves, AUC, RMSE, Gini, logloss, etc. Model interpretation (feature importance, SHAP, partial dependence plots). Best practices for production (file formats, drift monitoring, model monitoring). Hands-on case study with real data.
Target audience
Data scientists, AI engineers, data analysts, developers with existing basics in supervised machine learning
Prerequisites
Basic knowledge of Python and supervised machine learning (classification/regression), common model evaluation practices
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