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 advanced data classification algorithms for accurate predictions in business
- Optimize models with Scikit-learn and R during this professional certifying training
- Develop skills in handling imbalanced data for real data science projects
- Implement ensemble and boosting techniques to boost predictive performance
- Design end-to-end classification pipelines tailored to business needs
- Rigorous model evaluation to ensure professional reliability in the enterprise
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 1Advanced Fundamentals of Data Classification: Evaluation Metrics, Cross-Validation (Scikit-learn)
Discover advanced metrics such as AUC-ROC, F1-score, and precision-recall to rigorously evaluate your data classification models. Apply k-fold cross-validation techniques with Scikit-learn on real datasets. Complete practical exercises on business cases to identify common pitfalls. Build your first comprehensive evaluation reports, integrating visualizations with Matplotlib for educational and professional analysis.
Module 2Supervised Data Classification Algorithms: SVM, Optimized KNN (Scikit-learn, Hyperparameters)
Dive into non-linear SVM and weighted KNN using Scikit-learn. Tune hyperparameters with GridSearchCV and RandomizedSearchCV on complex datasets. Test on real-world cases like banking fraud detection. Develop optimized scripts to scale to large volumes. Analyze the impact of RBF and polynomial kernels on performance. Produce enterprise-ready deliverables with quantified metrics and comparative visualizations.
Module 3Data Classification in R: Decision Trees, Random Forest (caret, randomForest packages)
Master decision trees and Random Forest in R using the caret and randomForest packages for robust data classification. Apply bagging and boosting techniques on real enterprise data. Perform automatic tuning exercises via trainControl. Compare results with Scikit-learn for a hybrid approach. Generate automated reports with ggplot2 charts. Integrate practical cases like customer segmentation for actionable deliverables.
Module 4Advanced Data Classification Management: Imbalanced Classes, SMOTE, XGBoost (Scikit-learn, R)
Handle imbalanced datasets in data classification with SMOTE, undersampling, and oversampling using Scikit-learn and R. Implement XGBoost and LightGBM for superior performance. Test on real scenarios like churn prediction. Optimize decision thresholds to maximize recall. Develop complete pipelines with imbalanced-learn. Produce sensitivity analyses and detailed business reports for immediate enterprise application.
Module 5Data Classification Deployment: MLflow Pipelines, SHAP Interpretability (Scikit-learn, R, Production)
Deploy your data classification models in production with MLflow and Docker. Integrate interpretability using SHAP and LIME in Scikit-learn and R. Simulate AWS or Azure cloud environments on critical business cases. Automate CI/CD pipelines for rapid implementation. Evaluate robustness against data drifts. Finalize with complete deliverables including Streamlit dashboards and executive reports for immediate professional adoption.
Evaluation method
- Daily technical quizzes on metrics and algorithms
- Final end-to-end classification project on real dataset
- Peer-review evaluation and feedback from Qualiopi-certified trainer
Learning method
- Hands-on practical exercises with Jupyter and RStudio
- Concrete case studies from data-driven companies
- Live Q&A sessions for personalized issue resolution
- Unlimited access to replays and post-training resources
Methods, materials and delivery
The Training Data Classification - Master Advanced Predictive Algorithms 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 Data Classification - Master Advanced Predictive Algorithms 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 Data Classification - Master Advanced Predictive Algorithms 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 Data Classification - Master Advanced Predictive Algorithms training cost?+
The individual price is $5,775 (USD). A detailed quote is sent within one business day.
How long is the Training Data Classification - Master Advanced Predictive Algorithms 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?+
Mastery of Python, Scikit-learn, R, descriptive statistics, and basic supervised Machine Learning
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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