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 - Master Practical ML in Python training are: Proficiency in Python, advanced use of NumPy and Pandas, basics in statistics and initial ML models.. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training scikit-learn - Master Practical ML in Python training is available in in-person. The format can be adapted to your needs (inter, intra, custom).

Training scikit-learn - Master Practical ML in P... | 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 - Master Practical ML in Python 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 - Master Practical ML in Python 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 - Master Practical ML in Python 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 access terms and deadlines

Training objectives

Master preprocessing and feature engineering with scikit-learn

Implement and evaluate high-performing supervised models

Apply unsupervised algorithms for data exploration

Optimize hyperparameters via GridSearch and RandomizedSearch

Build end-to-end pipelines for automated ML workflows

Deploy scalable models with scikit-learn in production

Training program

Module 1Preprocessing and Features: Clean and Transform Your Datasets with scikit-learn

Dive into scikit-learn tools to scale, encode, and impute data; handle Imputer, StandardScaler, and OneHotEncoder on real datasets; complete practical exercises on Kaggle; build your first pipelines to automate workflows; gain immediate efficiency and prepare ML-ready data.

Module 2Supervised Models: Regression and Classification with scikit-learn

Explore LinearRegression, DecisionTree, and RandomForest; train models on real cases like housing price prediction; evaluate with metrics like RMSE and accuracy; code complete Jupyter scripts; test on validation data; acquire skills to solve real business problems right away.

Module 3Evaluation and Validation: Cross-Validation and Advanced Metrics in scikit-learn

Master KFold, StratifiedKFold, and metrics like ROC-AUC and Precision-Recall; analyze learning curves on varied datasets; detect overfitting with Learning Curves; practice interactive exercises; generate visual reports with matplotlib; enhance model reliability for solid data-driven decisions.

Module 4Unsupervised Learning: Clustering and Dimensionality Reduction with scikit-learn

Explore KMeans, DBSCAN, and PCA to segment customers or visualize high-dimensional data; apply to real e-commerce datasets; evaluate with Silhouette Score; code collaborative notebooks; generate actionable insights; transform raw data into business opportunities with powerful, accessible methods.

Module 5Tuning and Pipelines: Optimize and Deploy with scikit-learn

Build complete Pipelines with ColumnTransformer; tune hyperparameters via GridSearchCV; integrate with joblib for deployment; simulate an end-to-end churn prediction project; export production-ready models; leave with a concrete portfolio and skills to scale ML projects in teams.