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Training: Mastering XGBoost: Advanced Modeling for Data Science

Ref: DVQ698
8 people max.
From $3,465 HT / per person
Pay in 3 installments · On-site on request · +$540 with certification exam
3 days
Remote

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Learning objectives

  • Understand the functioning and principles of XGBoost
  • Master the installation, use, and configuration of the XGBoost package in Python
  • Know how to prepare, clean, and transform data for XGBoost
  • Perform regression and classification tasks with XGBoost
  • Optimize model performance through hyperparameter tuning and cross-validation
  • Interpret and leverage results using explainability and robust evaluation
  • Use XGBoost on real-world cases and large data volumes

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 this upskilling, your team accumulates a technological gap that translates directly into productivity loss.

  • Organizations that don't train their talents on key topics see their competitiveness drop.

  • Every quarter without training is a gap widening with competitors who invest.

  • The cost of inaction quickly exceeds that of well-targeted training.

Kheireddin KADRI
Kheireddin KADRI

Learni trainer · Data & AI expert

73%productivity gap
×3cost of inaction

Program

Module 1XGBoost Fundamentals and Data Preparation

Presentation of XGBoost and its history. Principle of gradient boosting, differences with Random Forest, introduction to the Python library. Types of problems addressed. Preparation and cleaning of datasets, handling missing variables, transformation and encoding. Manipulation of XGBoost DMatrix. First trials of simple classification and regression models. Practical examples step by step.

Module 2Parameterization, Training, and Optimization of XGBoost Models

Detailed presentation of main XGBoost hyperparameters (max_depth, learning_rate, n_estimators, subsample, colsample_bytree, etc.) and their effects. Setting up automatic cross-validation and Grid Search for optimization. Identifying and managing overfitting. Using appropriate evaluation metrics (AUC, accuracy, log-loss, RMSE). Practical exercises with advanced tuning and result interpretation.

Module 3Explainability, Advanced Applications & Deployment

Explainability of XGBoost models (feature importance, SHAP, visualization). Preventing and analyzing overfitting, handling imbalanced data, advanced preprocessing techniques for XGBoost. Sectoral examples (finance, health, industry). Export, saving, and deploying an XGBoost model in production. Best practices for scaling (big data). Practical cases and mini-projects throughout the day.

Evaluation method

  • Completion of a mini real-world application project with explainability report
  • Quiz to validate acquired knowledge at the end of the session
  • Collective correction of exercises during the training

Learning method

  • Alternation of theoretical presentations and hands-on practice on computer
  • Application exercises from real business cases
  • Detailed course materials, provided Python scripts, and educational datasets

Methods, materials and delivery

The Training: Mastering XGBoost: Advanced Modeling for Data Science 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: Mastering XGBoost: Advanced Modeling for Data Science 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: Mastering XGBoost: Advanced Modeling for Data Science 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: Mastering XGBoost: Advanced Modeling for Data Science training cost?+
The individual price is $3,465 (USD). A detailed quote is sent within one business day.
How long is the Training: Mastering XGBoost: Advanced Modeling for Data Science training?+
The training lasts 3 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?+
Python basics, knowledge of supervised learning, machine learning concepts
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

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