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.
1 seat held for the next session
30:00Held for another
Request a call back and an advisor reaches you within 24 business hours, Monday to Saturday.
Trusted by — schools, companies and industrial groups
Every program built with a dedicated engineer
Learning objectives
- Understand the Scikit-learn ecosystem for machine learning
- Apply the main classification, regression, and clustering methods with Scikit-learn
- Implement data processing and modeling pipelines
- Evaluate and optimize machine learning models
- Deploy and automate reproducible machine learning workflows
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 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.
Program
Module 1Introduction to Machine Learning and Scikit-learn
Overview of machine learning, introduction to Scikit-learn, installation and discovery of the ecosystem, exploration of built-in datasets, typical machine learning pipeline (preparation, modeling, evaluation), hands-on with Jupyter Notebook for exercises.
Module 2Data Preparation and Classification Methods
Data cleaning and transformation with Scikit-learn, imputation techniques, variable encoding, standardization and normalization, train/test split, implementation of classification algorithms (logistic regression, SVM, k-NN, decision trees), performance metrics (precision, recall, F1-score), ROC curve, cross-validation.
Module 3Regressions, Clustering, and Model Optimization
Linear and polynomial regression, Ridge/Lasso regression, introduction to clustering (k-means, DBSCAN), cluster evaluation, pipeline construction, hyperparameter search (GridSearchCV, RandomizedSearchCV), handling overfitting and underfitting, introduction to model export and deployment. Practical case: end-to-end pipeline on a real dataset.
Evaluation method
- Knowledge validation MCQ
- Mini-projects on public datasets supervised throughout the training
- Oral presentation of a mini-project during the last session
Learning method
- Interactive course via videoconference
- Live demonstrations on Jupyter Notebook
- Progressive practical exercises for each module
- Concrete case studies and real datasets
- Access to digital course materials and code examples
Methods, materials and delivery
The Mastering Scikit-learn: Practical Training in Machine Learning with Python 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 Mastering Scikit-learn: Practical Training in Machine Learning with 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 Mastering Scikit-learn: Practical Training in Machine Learning with 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 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.
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.
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.
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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FAQ
Frequently asked questions
How much does the Mastering Scikit-learn: Practical Training in Machine Learning with Python training cost?+
The individual price is $3,675 (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 Mastering Scikit-learn: Practical Training in Machine Learning with Python 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?+
Basic knowledge of Python and statistics
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
This training across cities
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