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.
Learning objectives
- Master the fundamentals of Scikit-learn for professional certifying projects.
- Develop skills in preprocessing and feature engineering with Scikit-learn.
- Implement classification and regression models adapted to enterprise needs.
- Optimize hyperparameters for superior predictive performance.
- Integrate Scikit-learn into simple and effective MLOps workflows.
- Build end-to-end pipelines for certifying data analyses.
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.
Program
Module 1Scikit-learn Fundamentals: Installation and First Models (Jupyter, Iris Datasets)
Discover Scikit-learn through quick installation in a Jupyter environment, explore classic datasets like Iris for your first classification models, complete practical exercises on data scalability, manipulate essential APIs with concrete examples, and generate your first prediction reports to validate beginner professional machine learning skills.
Module 2Scikit-learn Preprocessing: Data Cleaning and Transformation (Pandas, Imputers)
Dive into preprocessing with Scikit-learn, clean real datasets using Imputers and Scalers, apply feature engineering techniques like OneHotEncoder, chain transformations in automated pipelines, test on concrete enterprise cases, and generate datasets ready for modeling, strengthening your certifying skills in professional data preparation.
Module 3Supervised Models in Scikit-learn: Classification and Regression (LogisticRegression, RandomForest)
Implement supervised models with Scikit-learn, train LogisticRegression on binary data and RandomForest for multiclass predictions, evaluate using metrics like accuracy and F1-score, fine-tune on real e-commerce datasets, compare performances in practical exercises, and deploy your first prototypes for enterprise applications, boosting your beginner ML expertise.
Module 4Scikit-learn Optimization: Hyperparameters and Cross-Validation (GridSearch, CrossValidation)
Optimize your Scikit-learn models with GridSearchCV to tune hyperparameters, master K-Fold cross-validation on large datasets, analyze learning curves to avoid overfitting, apply to business predictive cases, generate detailed optimization reports, and integrate TensorFlow/PyTorch as complements for hybrid pipelines, certifying your beginner MLOps skills.
Module 5Scikit-learn MLOps Pipelines: Deployment and Monitoring (Pipeline, Model Persistence)
Build end-to-end Scikit-learn pipelines with make_pipeline, persist models via joblib for deployment, integrate basic monitoring with custom metrics, test on real enterprise scenarios including PyTorch for scalability, deploy interactive prototypes via Streamlit, and finalize with a deliverable certifying project, ensuring complete professional upskilling in machine learning and MLOps.
Evaluation method
- Daily interactive quizzes on Scikit-learn to validate learning.
- Final ML pipeline project on a real dataset, graded by experts.
- Qualiopi certifying attestation with evaluated skills.
Learning method
- 70% practical exercises on Jupyter and authentic datasets.
- Replayable tutorial videos and interactive PDF materials.
- Live Q&A sessions with a certified MLOps coach.
- 6-month post-training platform access for revisions.
Methods, materials and delivery
The Training Scikit-learn 2026 - Master Practical 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 Scikit-learn 2026 - Master Practical 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 Scikit-learn 2026 - Master Practical 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
★★★★★
« cool, j'ai appris des trucs »
★★★★★
« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »
★★★★★
« la formation dev etait intense mais grave bien. merci Anthony Khelil »
★★★★★
« 😊👍 »
★★★★★
« bien 👍 »
★★★★★
« Allan Busi t'es au top, continue comme ça. formation géniale »
★★★★★
« cool, j'ai appris des trucs »
★★★★★
« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »
★★★★★
« la formation dev etait intense mais grave bien. merci Anthony Khelil »
★★★★★
« 😊👍 »
★★★★★
« bien 👍 »
★★★★★
« Allan Busi t'es au top, continue comme ça. formation géniale »
★★★★★
« cool, j'ai appris des trucs »
★★★★★
« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »
★★★★★
« la formation dev etait intense mais grave bien. merci Anthony Khelil »
★★★★★
« 😊👍 »
★★★★★
« bien 👍 »
★★★★★
« Allan Busi t'es au top, continue comme ça. formation géniale »
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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