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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 Scikit-learn - Implementing High-Performance ML Models 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 Scikit-learn - Implementing High-Performance ML Models 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 Scikit-learn - Implementing High-Performance ML Models 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.
Immediate immersion in Scikit-learn through advanced installation and creation of real company datasets, in-depth exploration of preprocessing with StandardScaler, OneHotEncoder, and custom pipelines to streamline workflows, practical implementation of algorithms like RandomForest, SVM, and XGBoost on concrete cases such as churn prediction or credit scoring, hyperparameter optimization using GridSearchCV and RandomizedSearchCV with cross-validation, precise evaluation metrics (ROC-AUC, F1-score, RMSE), saving and loading models for production, hands-on exercises on your own dataset with expert feedback for skills directly applicable in a team setting.
Target audience
Data analysts, data scientists, machine learning engineers seeking professional skill enhancement
Prerequisites
Mastery of Python with pandas and NumPy, basic knowledge of supervised machine learning
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