RMachine Learning with R Training
Build and evaluate machine learning models in R with tidymodels in 40 live hours
- Duration
- 40 hours
- Format
- Live online
- Modules
- 10 modules
- Certificate
- On completion
- ✓40 hours of live online instructor-led training in scheduled batches
- ✓Modern R workflow using tidymodels (recipes, parsnip, rsample, tune, yardstick)
- ✓Regression, classification, tree ensembles, boosting, clustering and PCA
- ✓Resampling, tuning, calibration and model comparison done properly
- ✓Explainable models with DALEX and variable importance
- ✓Assure Success course-completion certificate
About the course
What this course covers
Machine Learning with R is for analysts and statisticians who already know basic R and want to build predictive models in the same environment. R has a long tradition of rigorous modelling, and the tidymodels framework gives a consistent interface across algorithms, so you spend your time on the problem rather than on each package's quirks.
You will preprocess data with recipes, split and resample with rsample, specify models through parsnip, tune them with the tune package and judge them with yardstick. Algorithms covered include penalised regression, logistic regression, decision trees, random forests, XGBoost, k-means and PCA. We pay particular attention to honest evaluation, class imbalance and explaining model output to non-technical people.
There is no external vendor exam. You finish with a capstone and receive an Assure Success course-completion certificate.
Who should attend
- ● Analysts and statisticians in pharma, healthcare, insurance and banking who work in R
- ● Participants of R for Data Science or Data Science with R who want to go deeper into modelling
- ● Researchers who need predictive models alongside inference
- ● Python users who must collaborate with R-based teams
- ● Graduates in statistics, biostatistics, economics or mathematics
Prerequisites
- Working knowledge of R and the tidyverse (data frames, dplyr, ggplot2)
Key skills
Skills you will walk away with
tidymodels workflow
Build reproducible modelling workflows from recipe to fitted workflow.
Regression and regularisation
Fit linear models with ridge and lasso penalties and read the results.
Classification
Train logistic regression, trees, random forests and boosted models, and choose thresholds.
Resampling and tuning
Use cross-validation and grid or Bayesian tuning to select models fairly.
Evaluation metrics
Compare models with RMSE, ROC-AUC, PR-AUC, F1 and calibration plots.
Unsupervised learning
Apply k-means, hierarchical clustering and PCA to segment data.
Curriculum
Module-by-module syllabus
10 modules · 40 topics · 40 hours
1Module 1: The Modelling Workflow in R4 topics ▾
Learning objective: Set up a sound modelling process.
- ▸ Problem types and baselines
- ▸ Data splitting with rsample
- ▸ Avoiding leakage
- ▸ Overview of tidymodels
2Module 2: Preprocessing with recipes4 topics ▾
Learning objective: Prepare features consistently.
- ▸ Imputation and encoding steps
- ▸ Scaling, transformations and dummy variables
Schedules
Upcoming live batches
Enroll now and choose your batch dates
Send an enquiry and we'll reserve your seat in the next batch.
Certification
How you get certified
- 1Enrol in a scheduled live batch and confirm your R setup
- 2Attend sessions and complete the modelling assignments
- 3Finish the capstone and present it for review
- 4Meet the attendance and assessment criteria for the batch
- 5Receive your Assure Success course-completion certificate
Career outcomes
Roles this opens up
Data Scientist (R)
₹6–14 LPA (approx.)Builds and validates predictive models in R-heavy teams.
Statistical Programmer / Modeller
₹5–12 LPA (approx.)Supports statistical modelling in pharma, insurance and risk functions.
Risk / Credit Analyst (Modelling)
₹5–12 LPA (approx.)Develops scorecards and propensity models in BFSI.
Research Data Analyst
₹4–9 LPA (approx.)Uses predictive modelling to support research findings.
Salary ranges are approximate figures for India and vary by city, company and experience.
Why this certification is worth it
Consistent interface
tidymodels removes the need to learn a different syntax for every algorithm.
Why Assure Success
Learn it once, pass it once
Trainers from the field
Every session is led by a practitioner who uses this material at work.
Batches that fit your week
Weekday and weekend batches in IST, scheduled for working professionals.
Exam-style practice
Practice questions and, where available, a full simulator free with the course.
Exam voucher help
Discounted vouchers and step-by-step help booking your exam.
Human support
WhatsApp support Mon–Sat, 9 AM–8 PM IST — a person answers.
Pay your way
UPI, cards, netbanking and EMI in India; international cards accepted.
FAQs
Questions learners ask us
Q.Do I need to know R before joining?
Yes, a working knowledge of R and the tidyverse is expected. R for Data Science is a good preparation.
Q.Why tidymodels and not caret?
tidymodels is the actively developed successor from the same lead developer and integrates with the tidyverse. We mention caret briefly because many older codebases still use it.
Q.Is this course different from Machine Learning with Python?
The concepts are the same; the tools differ. Pick the language your team or sector uses. Many learners eventually learn both.
Q.Does it cover deep learning?
No. It focuses on classical machine learning. Deep learning is covered in separate courses.
Q.Is there an official exam?
No. You get an Assure Success course-completion certificate after the capstone.
Q.Will I use large datasets?
Datasets are sized to run on a laptop. We discuss approaches for larger data without requiring a cluster.
Q.Is R good for production machine learning?
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Machine Learning with R Training