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Training XGBoost - Optimizing High-Performance ML Models

Ref: BGT959
10 people max.
From $4,620 HT / per person
On-site on request · +$540 with certification exam
4 days
Remote

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Equans
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Ubisoft
Microsoft
Aptar
ArcelorMittal
Equans
EDF
Ubisoft
Microsoft
Aptar
ArcelorMittal
Equans
EDF
Ubisoft
Microsoft
Aptar
ArcelorMittal
Equans
EDF
Ubisoft
Microsoft
Aptar
ArcelorMittal
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Learning 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
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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 ML models remain suboptimal, losing up to 30% accuracy on critical predictions such as customer churn or fraud detection, according to Kaggle studies.

  • This produces massive financial losses: the average bank forfeits 15% of annual revenue due to ineffective boosting.

  • In business, this stalls promotions and competitiveness, exposing you to agile competitors.

  • For data scientists and .NET developers, ignoring XGBoost blocks the transition to productive AI, risking career obsolescence.

  • Invest now to turn these risks into lasting strategic advantages.

Allan BUSI
Allan BUSI

Learni trainer · AI expert

73%productivity gap
×3cost of inaction

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.

Evaluation method

  • Daily interactive quizzes on XGBoost concepts
  • Practical projects with personalized feedback
  • Final exam and portfolio of deployed models

Learning method

  • Active pedagogical methods with 70% hands-on practice
  • Real case studies from .NET and data companies
  • Unlimited access to replays and post-training resources
  • Qualiopi certification validating intermediate skills

Methods, materials and delivery

The Training XGBoost - Optimizing High-Performance ML Models 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 Training XGBoost - Optimizing High-Performance ML Models 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 Training XGBoost - Optimizing High-Performance ML Models 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.

Step 1

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.
Step 2

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.
Step 3

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.
Step 4

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.

FAQ

Frequently asked questions

How much does the Training XGBoost - Optimizing High-Performance ML Models training cost?+
The price is $4,620 (USD) per participant. A detailed quote is sent within one business day.
How long is the Training XGBoost - Optimizing High-Performance ML Models training?+
The training lasts 4 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?+
Knowledge of C# or Python programming, basics of machine learning and statistics, experience with structured datasets
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.
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