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 Training scikit-learn - Optimize Your Advanced ML Models training are: Advanced proficiency in Python, pandas, NumPy, intermediate scikit-learn, supervised/unsupervised machine learning.. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training scikit-learn - Optimize Your Advanced ML Models training is available in remote. The format can be adapted to your needs (inter, intra, custom).

Training scikit-learn - Optimize Your Advanced M... | 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 Training scikit-learn - Optimize Your Advanced 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.

Teaching resources provided

To ensure the quality of the Training scikit-learn - Optimize Your Advanced ML Models 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 Training scikit-learn - Optimize Your Advanced ML Models 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

Master advanced pipelines and custom transformers

Optimize hyperparameters via GridSearchCV and RandomizedSearchCV

Implement expert feature engineering with PolynomialFeatures

Evaluate complex models via cross-validation and advanced metrics

Manage massive datasets with out-of-core learning

Integrate scikit-learn with TensorFlow and Streamlit deployment

Training program

Module 1scikit-learn Pipelines: Workflow Automation (Transformers, Imputers)

Dive into Pipeline and ColumnTransformer to chain preprocessing and modeling, configure advanced KNN imputers and robust scalers on real datasets, perform practical exercises to handle massive missing data, produce reproducible pipelines that boost performance from day one.

Module 2Expert Hyperparameter Tuning: GridSearch, BayesSearch (Optuna Integration)

Explore GridSearchCV and RandomizedSearchCV to tune SVM and RandomForest, integrate Optuna for ultra-fast Bayesian optimization, test on concrete cases like churn prediction, generate automated score reports, transform basic models into cross-validated champions in a few iterations.

Module 3Advanced Feature Engineering: PolynomialFeatures, Interactions (SHAP)

Create polynomial features and automated interactions with FeatureUnion, apply SelectKBest and RFE for expert selection, analyze impacts via SHAP on XGBoost, practice on Kaggle datasets to gain 20% accuracy, deliver production-ready notebooks.

Module 4Evaluation and Ensembling: Stacking, VotingClassifiers (ROC-AUC Metrics)

Deploy StackingClassifier and VotingRegressor for powerful ensembles, master advanced metrics like ROC-AUC and log-loss, simulate real fraud detection scenarios, calibrate probabilities with IsotonicRegression, achieve leaderboard rankings in the blink of an eye.

Module 5Scaling and Deployment: Out-of-Core, PMML, Streamlit (Big Data)

Process terabytes with TruncatedSVD and IncrementalPCA, export to PMML for legacy systems, deploy interactive Streamlit APIs, integrate with Docker and Kubernetes, finalize capstone project on million-row dataset, leave with deployed and certified portfolio.