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 regression suite algorithms in Scikit-learn and R for professional development
- Develop optimised regression pipelines for corporate data science projects
- Implement expert regularisation techniques and cross-validation on massive datasets
- Optimise predictive performance of certified regression suite models
- Analyse and interpret multivariate results for strategic decision-making
- Design robust regression suites tailored to business needs
- Acquire expert Data Science skills for professional certification
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 1Linear Regression Suite: Polynomial Extensions and Regularisation (Scikit-learn, R)
Dive into the expert foundations of regression suites with a review of generalised linear models, implement polynomial transformations via Scikit-learn and lm() in R, explore Ridge and Lasso to counter multicollinearity, complete exercises on real datasets like Boston Housing, produce reports on adjusted coefficients, and visualise learning curves to assess prediction stability in professional contexts.
Module 2Non-Linear Regression Suite: GAM and Splines (Scikit-learn, R mgcv)
Deepen non-linear regression suites by modelling complex relationships with Generalized Additive Models via mgcv in R and Scikit-learn pipelines, apply cubic splines and RBF kernels on time series data, test expert interactions, analyse significance via AIC/BIC, perform Monte Carlo simulations for robustness, and generate out-of-sample predictions with RMSE/MAE metrics suited to corporate data science cases.
Module 3Robust Regression Suite: Quantiles and Trees (Scikit-learn QuantileRegressor, R rq)
Master robust regression suites against outliers using Scikit-learn's QuantileRegressor and quantreg in R, integrate boosting trees like XGBoost for hybrid regressions, optimise hyperparameters via GridSearchCV and nested cross-validation, apply to noisy financial datasets, compare performance with QQ-plots and standardised residuals, and produce interactive Plotly dashboards for rapid team interpretation.
Module 4Ensembles in Regression Suite: Stacking and Bagging (Scikit-learn, R caret)
Build expert regression suite ensembles with stacking via StackingRegressor in Scikit-learn and caret in R, combine GLM, Random Forest, and Gradient Boosting to reduce variance, tune meta-models on hold-out sets, simulate advanced feature engineering scenarios like automated interactions, evaluate via permutation importance and SHAP values, and deliver reproducible Dockerised pipelines for certified data science production deployment.
Module 5Optimisation and Deployment of Regression Suite: MLOps (Scikit-learn Pipelines, R plumber)
Finalise your expertise in regression suites with Bayesian optimisation using Hyperopt and deployment via MLflow in Scikit-learn, expose REST APIs in R with plumber, integrate drift monitoring with Alibi Detect, test A/B on live predictions, draft executive reports with quantified business impacts, and prepare for professional certification via a capstone project on real corporate cases for lasting Data Science skills.
Evaluation method
- Advanced quizzes on algorithms and interpretations
- Practical case studies with real datasets
- Final regression suite project evaluated by experts
Learning method
- Alternating theory and 70% hands-on practice with real tools
- Large datasets and concrete business cases
- Unlimited video support and dedicated forums
- Post-training resources for expert review
Methods, materials and delivery
The Training Regression Suite - Optimising Predictions in Data Science 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 Regression Suite - Optimising Predictions in Data Science 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 Regression Suite - Optimising Predictions in Data Science 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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