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 XGBoost - Optimizing High-Performance ML Models training are: Knowledge of C# or Python programming, basics of machine learning and statistics, experience with structured datasets. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training XGBoost - Optimizing High-Performance ML Models training is available in in-person. The format can be adapted to your needs (inter, intra, custom).

Training XGBoost - Optimizing High-Performance ML… | 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 XGBoost - Optimizing 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.

Teaching resources provided

To ensure the quality of the Training XGBoost - Optimizing High-Performance 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 XGBoost - Optimizing High-Performance 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 XGBoost parameters for precise predictive models

Develop feature engineering pipelines tailored to business needs

Implement cross-validation and hyperparameter tuning techniques to optimize performance

Design XGBoost solutions integrable into certified .NET environments

Evaluate and deploy XGBoost models in production with solid professional skills

Analyze boosting metrics for data-driven decision-making in business

Training program

Module 1XGBoost Fundamentals: Installation and First Models (Python Tools, Real Datasets)

Discover XGBoost through its quick installation with pip and conda, configure your first decision trees on Iris and Titanic datasets, perform basic training with early stopping to avoid overfitting, make predictions and visualize feature importances via matplotlib, produce your first performance reports with accuracy and ROC-AUC, apply practical exercises on real business cases to consolidate learning from the first day.

Module 2Advanced XGBoost: Feature Engineering and Tuning (scikit-learn Pipelines, Grid Search)

Dive into feature optimization with categorical encoding and scaling, build automated pipelines integrating XGBoost and preprocessing, test grid search and random search to hyperparameterize learning rate, max_depth, and subsample, analyze learning curves to detect underfitting, deploy k-fold cross-validation on large datasets, generate detailed reports with SHAP to interpret predictions, implement practical cases inspired by Kaggle challenges adapted to professional contexts.

Module 3XGBoost in Production: .NET Integration and Deployment (ML.NET, Docker, APIs)

Integrate XGBoost into .NET workflows via ML.NET and Python calls, develop REST APIs with Flask to serve models, containerize with Docker for scalable deployment, manage production performance monitoring with Prometheus, optimize inference latency on large volumes, test robustness against data drift, produce interactive dashboards with Plotly Dash, apply exercises on real business scenarios for smooth and secure production deployment.

Module 4Expert XGBoost: Optimization and Advanced Cases (Ensembling, GPU, Bias Correction)

Master GPU acceleration with native XGBoost to process millions of samples, combine XGBoost in ensembles with stacking and bagging to surpass benchmarks, correct biases with reweighting and focal loss techniques, analyze residual errors for rapid iterations, deploy hybrid .NET-Python models in microservices, evaluate business impact via simulated ROI, finalize with a capstone project on a custom dataset including source code deliverable and full report for skills certification.