Rigorous trainer selection
Each trainer is validated on three criteria: hands-on field expertise, proven pedagogy and alignment with your industry.
- Triple validation: technical, pedagogical, sectoral.
- Minimum rating 4.8/5 over the last 12 sessions.
1 seat held for the next session
30:00Held for another
Request a call back and an advisor reaches you within 24 business hours, Monday to Saturday.
Trusted by — schools, companies and industrial groups
Every program built with a dedicated engineer
Learning objectives
- Master XGBoost to develop professional predictive models
- Optimize hyperparameters in certified enterprise contexts
- Implement scalable XGBoost pipelines with scikit-learn
- Analyze feature importance for business decisions
- Deploy XGBoost models in secure production environments
- Develop boosting skills for data competitions
- Design robust and interpretable ML solutions
Our social commitment
A school kit donated to a child for every training
To fight inequalities in access to education, Learni donates a complete school kit to a child in need for every training booked. You build your skills, a child heads back to school.
- Backpack, notebooks and essential supplies
- Distributed through our partner charities
- Included, at no extra cost to you
The Learni story
Founded by engineers and learning experts, Learni's mission is to make high-impact tech training accessible to teams everywhere. We work remotely with organizations across the US and Canada, in your time zone, to help teams upskill fast.
Don't let this gap widen
Why this program matters
Without mastering XGBoost, your predictive models lose an average of 15-25% accuracy versus Kaggle benchmarks, driving flawed business decisions that cost up to 30% of revenue from poorly predicted churn.
Data teams spend 40% more time on manual tuning, with 2 out of 5 incidents tied to undetected overfitting.
In 2024, 68% of data recruiters reject profiles lacking advanced boosting expertise, widening the competitive gap.
Each quarter without these skills exposes the company to estimated losses of 50k€ in missed opportunities.
Program
Module 1XGBoost Fundamentals: Installation and First Models (Python, DMatrix, early stopping)
Quick installation of XGBoost in a professional Python environment, hands-on with DMatrix objects for large datasets, building first classification and regression models on concrete business cases such as churn prediction, using early stopping to avoid overfitting, practical exercises on Kaggle datasets with cross-validation, generating reliable predictions and first performance graphs to evaluate business accuracy.
Module 2XGBoost Optimization: Tuning and Deployment (GridSearchCV, SHAP, production pipelines)
Advanced exploration of hyperparameters using GridSearchCV and RandomSearch to boost performance with up to 20% gain in accuracy, integration of XGBoost into scikit-learn pipelines for enterprise automations, interpretability analysis with SHAP to explain decisions to stakeholders, exercises on custom tuning with real sales or fraud datasets, simulated deployment in Docker for production environments, final deliverable: an optimized ready-to-use model with a complete business impact report.
Evaluation method
- Multiple-choice quiz (MCQ) to validate XGBoost and boosting concepts
- Practical implementation of a model on an enterprise dataset
- SHAP analysis and hyperparameter tuning evaluated by the trainer
Learning method
- Courses led by a certified expert data scientist
- Practical exercises on Kaggle datasets and real business cases
- Ongoing project: optimization of a complete predictive model
- Course materials with source codes and Jupyter notebooks
Methods, materials and delivery
The XGBoost Training - Optimizing Predictive Models in ML program is delivered onsite or remote (blended-learning, virtual classroom, remote presence). At Learni, an industry-certified training organization, every program is built to maximize skills acquisition regardless of the chosen format.
The trainer alternates between demonstrative, interrogative and active methods (through hands-on labs and/or scenarios). This pedagogical approach guarantees concrete learning that's immediately applicable at work.
Equipment required
For the smooth delivery of the XGBoost Training - Optimizing Predictive Models in ML program, the following equipment is required:
- Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, projector or interactive touch screen (for remote sessions)
- Training environments installed on workstations or accessible online
- Course materials, hands-on exercises and complementary resources
- Post-training access to materials and educational resources
For intra-company training on a site outside Learni, the client commits to providing all required teaching materials (computers, internet, etc.) for the smooth delivery of the program in line with the prerequisites in the communicated program.
* contact us for remote delivery feasibility** ratio varies depending on the program
Skills assessment methods
Assessment of skills acquired during the XGBoost Training - Optimizing Predictive Models in ML program is performed through:
- During training: case studies, hands-on labs and professional scenarios
- End of training: self-assessment questionnaire and skills evaluation by the trainer
- After training: completion certificate detailing acquired skills
Program accessibility
Learni is committed to making its programs accessible. All our programs are accessible to people with disabilities. Our teams are available to adapt the pedagogical methods to your specific needs. Please contact us for any adjustment request.
Enrollment terms and lead times
Registration is possible up to 48 business hours before the start of training. All our programs are built for corporate L&D budgets and delivered onsite or remotely.
Our method
Training quality, guaranteed at every step
Before, during, after: we frame the brief, introduce the trainer, tailor the content and measure impact. You stay in control from kickoff to wrap-up.
You meet the trainer beforehand
30-minute video call between you and the selected trainer to validate the fit, adjust content and clear any final doubts.
- Live briefing on goals and team context.
- Veto right — we swap the trainer for free if needed.
Content tailored to your context
No recycled slides. The syllabus is reworked from your real cases: tools, constraints, vocabulary, ongoing projects.
- Hands-on cases drawn from your stack and projects.
- Program co-written then validated by your team.
Continuous quality follow-up
Live evaluations, 30/90/180-day check-ins and a consolidation plan. If the impact misses the mark, we rework it.
- NPS, knowledge quizzes and skills self-assessment.
- Satisfaction guarantee: fully satisfied or free rework.
A simple promise: you don't pay to discover the trainer on day one. Everything is validated upfront, by you.
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FAQ
Frequently asked questions
How much does the XGBoost Training - Optimizing Predictive Models in ML training cost?+
The price is $2,625 (USD) per participant. A detailed quote is sent within one business day.
How long is the XGBoost Training - Optimizing Predictive Models in ML training?+
The training lasts 2 journées, available live online (US time zones) or on-site at your offices.
How is this training paid for?+
Most US teams pay directly through their company (L&D or training budget). We invoice in US dollars and accept bank transfer (ACH/wire) or card, with volume pricing for teams. A purchase order is welcome.
Are there any prerequisites?+
Proficiency in Python, basics in machine learning, pandas and numpy
Is a certificate delivered at the end?+
Yes. A Learni completion certificate is issued, along with the individual evaluation report.
Does Learni provide the equipment?+
No. A computer and stable internet connection are required for the participant. Learni provides the educational platform, the trainer and all course materials.
On-site & remote
This training across cities
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