🎁Azure · AWS · Google — 1 free certification per learner, up to $400.Get the offer →
← Back

scikit-learn Expert Training - Optimize Advanced ML Pipelines

Ref: MYL658
10 people max.
$5,040 HT / per person
−15% from 2 people−30% from 3 people−50% from 5 people
Pay in 3 installments · +$180/day onsite · +$540 with certification exam
4 days
Remote

Share in 2 clicks

EquansAptarArcelorMittalUbisoftINSEECLa PlateformeCESIEFREIEPSIINGETISMy Digital SchoolYnovEquansAptarArcelorMittalUbisoftINSEECLa PlateformeCESIEFREIEPSIINGETISMy Digital SchoolYnov

Learning objectives

  • Build optimized pipelines with ColumnTransformer and FeatureUnion.
  • Master hyperparameter tuning via GridSearchCV and RandomizedSearchCV.
  • Implement advanced ensembles like Stacking and Voting classifiers.
  • Optimize models for production with robust cross-validation.
  • Integrate scikit-learn into Big Data workflows via Joblib and Dask.
  • Evaluate and debug complex models with custom metrics.

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 expert mastery of scikit-learn, your pipelines waste 40% of time on manual preprocessing, your models lose 20-30% accuracy due to lack of advanced tuning—like 70% of junior data scientists who fail in production.

  • Risk of x3 cloud overcosts for rework, +25% team turnover from frustration, and missed opportunities against AI competition.

  • Invest 28h for x10 ROI in efficiency, avoid 50k€ annual losses from inefficient modeling.

Allan Busi
Allan Busi

Learni Trainer · Expert

73%productivity gap
×3cost of inaction

Program

Module 1Advanced Pipelines: Construction and Preprocessing with scikit-learn

Dive into creating robust pipelines using ColumnTransformer to handle imbalanced data, PolynomialFeatures for interactions, and smart imputers. Apply them to real datasets like Kaggle competitions, complete hands-on exercises to automatically transform your features, and produce production-ready deliverables that boost your performance from day one.

Module 2Hyperparameter Tuning: Expert GridSearchCV and RandomizedSearchCV

Explore Bayesian optimization strategies with Optuna integrated into scikit-learn, test on XGBoost and LightGBM via pipelines, refine searches with HalvingGridSearchCV to save time, work on concrete overfitting cases, generate automated reports of best parameters, and leave with optimized multi-core scripts that multiply your efficiency by 5.

Module 3Advanced Ensembles: Stacking, Voting, and Boosting with scikit-learn

Master StackingClassifier and Regressor to combine RandomForest, SVM, and neural nets. Implement custom Voting with dynamic weights, optimize HistGradientBoosting for massive datasets, apply to real problems like churn prediction, conduct collaborative exercises to hybridize models, and create deployable deliverables that outperform benchmarks by 15-20%.

Module 4Deployment and Scalability: Joblib, Dask, and scikit-learn Monitoring

Integrate scikit-learn with Dask to scale on clusters, save models with Joblib and secure pickle, monitor drift with partial_fit, deploy via Flask APIs on production-like datasets, complete an end-to-end final project, and obtain a concrete portfolio with performance dashboards that impress recruiters.

Evaluation method

  • Daily interactive quizzes
  • Practical projects per day
  • Final certifying project
  • Pair-programming feedback

Learning method

  • Real projects on Kaggle datasets
  • Hands-on Jupyter exercises
  • Industrial case studies
  • Production simulations

Methods, materials and delivery

The scikit-learn Expert Training - Optimize Advanced ML Pipelines 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 scikit-learn Expert Training - Optimize Advanced ML Pipelines 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 scikit-learn Expert Training - Optimize Advanced ML Pipelines 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
★★★★★

« 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
★★★★★

« 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.

Your professional training, anywhere

Let's build
your next
program.

30 minutes with a learning advisor. No commitment. No sales pitch dressed up as a demo.

Reply within 24 h · Industry-certified · Corporate funding
WhatsApp