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Training scikit-learn Expert - Optimize Advanced ML Models

Ref: TDV641
10 people max.
From $2,310 HT / per person
On-site on request · +$540 with certification exam
2 days
Remote

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Equans
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Microsoft
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Ubisoft
Microsoft
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Learning objectives

  • Master custom pipelines with ColumnTransformer
  • Optimize hyperparameters via GridSearchCV and RandomizedSearchCV
  • Implement advanced ensembles like RandomForest and XGBoost
  • Perform sophisticated feature engineering on massive datasets
  • Evaluate models with expert metrics and cross-validation
  • Deploy scalable models in production with joblib
A child walking to school with a backpack
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 expert mastery of scikit-learn, 70% of ML projects fail due to poorly optimized pipelines, wasting 20-30% of development time.

  • Risk underperforming models with accuracy <80%, financial losses from erroneous predictions (ex: 50k€/churn error), project delays up to 2 months, and falling behind agile competitors.

  • Avoid overcosts from manual tuning (x3 time), unmanaged feature biases (+40% false positives), and unstable deployments causing 15% downtime.

  • Invest 14h for immediate ROI: 25% accuracy gains, projects delivered 50% faster.

Allan BUSI
Allan BUSI

Learni trainer · AI expert

73%productivity gap
×3cost of inaction

Program

Module 1Pipelines and Feature Engineering: Robust Workflows (Scikit-learn, Pipeline, ColumnTransformer)

Build custom pipelines to automate preprocessing and modeling, handle real datasets with scaling, polynomial encoding, and feature selection via SelectKBest, complete exercises on customer churn prediction, generate optimized deliverables ready for integration, discover tips to accelerate your ML projects by 40%, while boosting your daily efficiency.

Module 2Optimization and Deployment: Advanced Model Tuning (GridSearchCV, Ensembles, joblib)

Dive into hyperparameter tuning with GridSearchCV and Bayesian Optimization, implement RandomForest, GradientBoosting, and stacking on real cases like fraud detection, evaluate performance via ROC-AUC and precision-recall, deploy serialized models with joblib for scalable APIs, produce comparative analysis reports, master techniques for up to 25% accuracy gains, and leave with an expert portfolio.

Evaluation method

  • Daily practical exercises, final multiple-choice quiz, certified personal project.

Learning method

  • 70% hands-on with Jupyter, 30% interactive theory, real business cases.

Methods, materials and delivery

The Training scikit-learn Expert - Optimize Advanced 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 scikit-learn Expert - Optimize Advanced 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 scikit-learn Expert - Optimize Advanced 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 scikit-learn Expert - Optimize Advanced ML Models training cost?+
The individual price is $2,310 (USD). The team / on-site group package is shown on the course page. A detailed quote is sent within one business day.
How long is the Training scikit-learn Expert - Optimize Advanced ML Models 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?+
Advanced proficiency in Python, pandas, NumPy, intermediate scikit-learn, supervised and unsupervised machine learning.
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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