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Training XGBoost 2026 - Optimize predictions in machine learning

Ref: ESR111
10 people max.
5250€ HT / per person
−15% from 2 people−30% from 3 people−50% from 5 people
Pay in 3 installments · +$170/day onsite · +$500 with certification exam
5 journées
distanciel

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Learning objectives

  • Master XGBoost 2026 to develop professional predictive models
  • Optimize XGBoost hyperparameters in a certified enterprise context
  • Implement scalable XGBoost pipelines for big data
  • Design advanced boosting strategies with XGBoost
  • Evaluate and deploy XGBoost models in secure production
  • Develop certified skills in XGBoost for data science
  • Integrate XGBoost into automated enterprise workflows

The Learni story

Founded by passionate learning and innovation experts, Learni's mission is to make professional training accessible to everyone, anywhere in the world. Our team operates in major hubs — London, New York, Boston — and internationally, to support talents and organizations in upskilling.

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.

Fouzi Benzidane
Fouzi Benzidane

Learni Trainer · Expert

73%productivity gap
×3cost of inaction

Program

Module 1XGBoost 2026 Fundamentals: Installation and First Predictive Models (Python, DMatrix)

Quick installation of XGBoost 2026 in an Anaconda environment, creation of real datasets for enterprise data scientists, training of first classification and regression models with XGBClassifier and XGBRegressor, practical exercises on concrete cases like customer churn prediction, visualization of generated decision trees, first manual adjustments of hyperparameters to boost performance from the first day.

Module 2XGBoost 2026 Optimization: Hyperparameters and Cross-Validation (GridSearch, Early Stopping)

In-depth exploration of key XGBoost 2026 hyperparameters like learning_rate and max_depth, setting up automated cross-validation with scikit-learn, using early stopping to avoid overfitting on large datasets, practical workshops on Bayesian optimization with Optuna, comparison of ROC-AUC scores before/after tuning, production of performance reports for certified professional projects.

Module 3Advanced XGBoost 2026 Pipelines: Feature Engineering and Scaling (Pipeline, SHAP)

Building end-to-end pipelines integrating preprocessing with ColumnTransformer, XGBoost-specific feature engineering like interactions and embeddings, model interpretability via SHAP and LIME for business explanations, exercises on real adapted Kaggle datasets for enterprise, workflow automation with Dask for big data, generation of deliverables like interactive dashboards to validate skill contributions.

Module 4Ensemble Strategies with XGBoost 2026: Stacking and Hybrid Boosting (LightGBM Comparison)

Implementation of stacking with XGBoost 2026 as base and meta-learner, hybridization with LightGBM for superior performance, bias and variance management via custom bagging, concrete use cases in finance and e-commerce, collaborative workshops to model real-time predictions, quantified comparative evaluation of precision gains, preparation of Dockerized deployments for production environments.

Module 5XGBoost 2026 Deployment: Production and Monitoring (FastAPI, MLflow)

Deployment of XGBoost 2026 models via secure FastAPI APIs, integration of MLflow for experiment tracking and versioning, real-time monitoring with Prometheus to detect drift, final exercises on the enterprise red thread project, robustness tests on unforeseen data, delivery of certificates and action plans for sustainable skills, post-training assistance for immediate implementations.

Evaluation method

  • Technical MCQ on XGBoost 2026 and validation of acquired skills
  • Evaluation through practical projects and model optimization
  • Defense of the complete pipeline in front of the expert trainer

Learning method

  • Sessions led by active data scientist trainers
  • Hands-on exercises on real business datasets and Kaggle
  • Progressive red thread project with certified XGBoost 2026
  • Complete pedagogical support and online Jupyter resources

Methods, materials and delivery

The Training XGBoost 2026 - Optimize predictions in machine learning program is delivered onsite or remote (blended-learning, e-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 2026 - Optimize predictions in machine learning 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 2026 - Optimize predictions in machine learning 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

Learni programs are available inter-company and intra-company, onsite or remote. Enrollments are possible up to 48 business hours before the program starts. Our programs are eligible for corporate funding paths. Contact us to discuss your training project and funding options.

Verified reviews

What our learners

4.9 · +100 verified reviews
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« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
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« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
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« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
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« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
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« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
Read all reviews
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.

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