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 to develop professional ML skills in a business environment.
- Implement classification and regression algorithms adapted to real-world cases.
- Prepare and clean datasets to optimize the performance of certifying models.
- Evaluate and select the best models using standardized metrics.
- Deploy simple Machine Learning pipelines in a professional environment.
- Obtain a valuable certification to boost your career in data science.
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 1Scikit-learn Fundamentals: Installation and Dataset Manipulation (Python, Jupyter, pandas)
Discover Scikit-learn 2026 through guided installation on Jupyter Notebook, manipulate your first datasets with pandas and numpy to explore structures and descriptive statistics, create basic visualizations with matplotlib, clean real company data, and produce an initial data quality report, all through interactive remote practical exercises to solidify the foundations from day one.
Module 2Scikit-learn Preprocessing: Feature Engineering and Scaling (StandardScaler, pipelines)
Dive into preprocessing with Scikit-learn 2026, apply transformations like StandardScaler and MinMaxScaler on varied datasets, build your first automated pipelines to handle categorical encodings and missing value imputation, test on real e-commerce company cases, generate engineered features to boost models, and validate each step with hands-on exercises, ensuring optimal professional data preparation.
Module 3Supervised Algorithms in Scikit-learn: Regression and Classification (LinearRegression, LogisticRegression)
Master supervised algorithms in Scikit-learn 2026 by implementing linear regression models to predict sales or prices, move to classification with LogisticRegression on Iris and Titanic datasets, train-optimize using train_test_split, interpret coefficients and biases, apply to real business scenarios like customer churn, and produce prediction visualizations, through practical workshops for intuitive and professional understanding.
Module 4Evaluation and Tuning in Scikit-learn: Cross-Validation and GridSearch (metrics, hyperparameters)
Rigorous evaluation of your Scikit-learn 2026 models using accuracy, precision, recall, and ROC-AUC, implement K-Fold cross-validation for enhanced robustness, optimize hyperparameters via GridSearchCV on RandomForest and SVM, compare performances on company benchmarks, generate detailed reports with classification_report, and iterate on data-driven use cases, strengthening skills in selecting reliable models for certifying projects.
Module 5Pipelines and Deployment in Scikit-learn: Basic MLOps (joblib, Streamlit, TensorFlow integration)
Finalize with end-to-end Scikit-learn 2026 pipelines, integrate PyTorch and TensorFlow for simple hybridizations, save models via joblib and pickle, deploy a predictive app on Streamlit remotely, test in real company conditions, produce a complete deliverable including source code and interactive dashboard, and prepare for certification via a personal capstone project, for a smooth transition to professional MLOps practices.
Evaluation method
- Daily interactive quizzes on the remote platform to validate immediate learning.
- Final practical project on a company dataset, graded by expert trainer.
- Qualiopi certifying attestation delivered after 80% success in evaluations.
Learning method
- 70% hands-on with real exercises and open-source company datasets.
- 30% concise theory via videos and interactive materials.
- Pair work to foster exchanges and peer feedback.
- Unlimited access for 6 months to the platform with bonus MLOps resources.
Methods, materials and delivery
The Training Scikit-learn 2026 - Mastering the Basics of Machine Learning 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 2026 - Mastering the Basics of 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 - Mastering the Basics of 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
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 Training Scikit-learn 2026 - Mastering the Basics of Machine Learning training cost?+
The individual price is $5,775 (USD). A detailed quote is sent within one business day.
How long is the Training Scikit-learn 2026 - Mastering the Basics of Machine Learning training?+
The training lasts 5 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 (syntax, lists, functions), simple use of pandas and numpy, elementary notions of descriptive 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.
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