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 scikit-learn Expert Training - Optimize Advanced ML Pipelines training are: Mastery of Python, NumPy, Pandas, scikit-learn basics, and supervised/unsupervised machine learning.. A preliminary interview validates your eligibility.

Is the training available in person?

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

scikit-learn Expert Training - Optimize Advanced... | 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 scikit-learn Expert Training - Optimize Advanced ML Pipelines 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 scikit-learn Expert Training - Optimize Advanced ML Pipelines 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 scikit-learn Expert Training - Optimize Advanced ML Pipelines 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

Build optimized pipelines with ColumnTransformer and FeatureUnion.

Master hyperparameter tuning via GridSearchCV and RandomizedSearchCV.

Implement advanced ensembles like Stacking and Voting classifiers.

Optimize models for production with robust cross-validation.

Integrate scikit-learn into Big Data workflows via Joblib and Dask.

Evaluate and debug complex models with custom metrics.

Training program

Module 1Advanced Pipelines: Construction and Preprocessing with scikit-learn

Dive into creating robust pipelines using ColumnTransformer to handle imbalanced data, PolynomialFeatures for interactions, and smart imputers. Apply them to real datasets like Kaggle competitions, complete hands-on exercises to automatically transform your features, and produce production-ready deliverables that boost your performance from day one.

Module 2Hyperparameter Tuning: Expert GridSearchCV and RandomizedSearchCV

Explore Bayesian optimization strategies with Optuna integrated into scikit-learn, test on XGBoost and LightGBM via pipelines, refine searches with HalvingGridSearchCV to save time, work on concrete overfitting cases, generate automated reports of best parameters, and leave with optimized multi-core scripts that multiply your efficiency by 5.

Module 3Advanced Ensembles: Stacking, Voting, and Boosting with scikit-learn

Master StackingClassifier and Regressor to combine RandomForest, SVM, and neural nets. Implement custom Voting with dynamic weights, optimize HistGradientBoosting for massive datasets, apply to real problems like churn prediction, conduct collaborative exercises to hybridize models, and create deployable deliverables that outperform benchmarks by 15-20%.

Module 4Deployment and Scalability: Joblib, Dask, and scikit-learn Monitoring

Integrate scikit-learn with Dask to scale on clusters, save models with Joblib and secure pickle, monitor drift with partial_fit, deploy via Flask APIs on production-like datasets, complete an end-to-end final project, and obtain a concrete portfolio with performance dashboards that impress recruiters.