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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 Master LightGBM: Complete Training for Advanced Machine Learning Modeling 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 Master LightGBM: Complete Training for Advanced Machine Learning Modeling 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 Master LightGBM: Complete Training for Advanced Machine Learning Modeling 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.
Presentation of boosting algorithms, focus on LightGBM and its architecture (leaf-wise, histogram-based learning). Installation of LightGBM. Data management, cleaning, and transformation with Pandas and NumPy: encoding categorical variables, handling missing values. First hands-on experience with LightGBM for a simple classification task.
Detailed exploration of key LightGBM hyperparameters (num_leaves, max_depth, learning_rate, etc.), analysis of interactions and impact on performance. Automatic optimization with GridSearchCV and RandomizedSearchCV. Diagnosis of common errors, overfitting management (early stopping, regularization). Practical cases on open-source datasets.
LightGBM model interpretation techniques (feature importances, SHAP values, partial dependence plots). Adaptation to imbalanced data challenges: weighting, sampling, stratification. Use cases in regression and ranking, customization of cost functions. Introduction to deploying LightGBM models in data pipelines, integration into production systems (Flask API, batch scoring).
Target audience
Data scientists, analysts, machine learning engineers, Python developers wishing to deepen their skills in advanced predictive modeling with LightGBM.
Prerequisites
Basic knowledge of Python and supervised machine learning, familiarity with Pandas and NumPy.
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