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Training scikit-learn - Master Practical ML in Python

Ref: UUS704
10 people max.
$6,300 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
5 days
Remote

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Learning objectives

  • Master preprocessing and feature engineering with scikit-learn
  • Implement and evaluate high-performing supervised models
  • Apply unsupervised algorithms for data exploration
  • Optimize hyperparameters via GridSearch and RandomizedSearch
  • Build end-to-end pipelines for automated ML workflows
  • Deploy scalable models with scikit-learn in production

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 intermediate scikit-learn training, your models lose up to 40% accuracy due to suboptimal preprocessing, wasting weeks on manual debugging of dirty datasets.

  • Competing data teams deploy 3x faster thanks to automated pipelines, capturing 25% more market share via reliable predictions.

  • Risk obsolescence against generative AI?

  • 70% of data scientists report project delays from approximate tuning, costing 15k€/month in missed opportunities.

  • Master scikit-learn now to boost ML initiative ROI by 200%, avoid common pitfalls, and align with industry standards.

Allan Busi
Allan Busi

Learni Trainer · Expert

73%productivity gap
×3cost of inaction

Program

Module 1Preprocessing and Features: Clean and Transform Your Datasets with scikit-learn

Dive into scikit-learn tools to scale, encode, and impute data; handle Imputer, StandardScaler, and OneHotEncoder on real datasets; complete practical exercises on Kaggle; build your first pipelines to automate workflows; gain immediate efficiency and prepare ML-ready data.

Module 2Supervised Models: Regression and Classification with scikit-learn

Explore LinearRegression, DecisionTree, and RandomForest; train models on real cases like housing price prediction; evaluate with metrics like RMSE and accuracy; code complete Jupyter scripts; test on validation data; acquire skills to solve real business problems right away.

Module 3Evaluation and Validation: Cross-Validation and Advanced Metrics in scikit-learn

Master KFold, StratifiedKFold, and metrics like ROC-AUC and Precision-Recall; analyze learning curves on varied datasets; detect overfitting with Learning Curves; practice interactive exercises; generate visual reports with matplotlib; enhance model reliability for solid data-driven decisions.

Module 4Unsupervised Learning: Clustering and Dimensionality Reduction with scikit-learn

Explore KMeans, DBSCAN, and PCA to segment customers or visualize high-dimensional data; apply to real e-commerce datasets; evaluate with Silhouette Score; code collaborative notebooks; generate actionable insights; transform raw data into business opportunities with powerful, accessible methods.

Module 5Tuning and Pipelines: Optimize and Deploy with scikit-learn

Build complete Pipelines with ColumnTransformer; tune hyperparameters via GridSearchCV; integrate with joblib for deployment; simulate an end-to-end churn prediction project; export production-ready models; leave with a concrete portfolio and skills to scale ML projects in teams.

Evaluation method

  • Daily interactive quizzes on key concepts
  • Graded practical exercises with personalized feedback
  • Final team project on real dataset with oral presentation
  • Qualiopi certification awarded based on acquired skills

Learning method

  • 70% hands-on in Jupyter workshops on real business cases
  • 30% theory with live demos and experience sharing
  • Small groups of max 10 for individualized support

Methods, materials and delivery

The Training scikit-learn - Master Practical ML in Python 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 scikit-learn - Master Practical ML in Python 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 - Master Practical ML in Python 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 eligible for corporate training budgets and employer-funded plans.

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.

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