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Machine Learning with R Training — brochure
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Assure Success
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Assure SuccessCourse brochure 2026Build and evaluate machine learning models in R with tidymodels in 40 live hours
Course fee
₹42,999₹69,499
Enroll online at assuresuccess.in or call +91 93517 82674
Scan to view the course and enroll · Fees as on 27 Oct 2026
About the course
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.
Consistent interface
tidymodels removes the need to learn a different syntax for every algorithm.
Inference plus prediction
Connects classical statistical thinking with machine learning practice.
Reproducible by design
Workflows and recipes make models easy to review and rerun.
Relevant to regulated sectors
Explainability and validation habits fit finance, insurance and pharma contexts.
Curriculum
The Modelling Workflow in R
Set up a sound modelling process.
Preprocessing with recipes
Prepare features consistently.
Regression Models
Predict numeric outcomes.
Classification Models
Predict categories.
Ensembles and Boosting
Use powerful tree-based models.
Resampling and Tuning
Choose models and settings fairly.
Imbalanced Classes and Calibration
Handle rare events.
Unsupervised Learning
Segment and compress data.
Explaining and Sharing Models
Communicate model behaviour.
Capstone Project
Deliver an end-to-end modelling project in R.
Your certificate
Enrol in a scheduled live batch and confirm your R setup
Attend sessions and complete the modelling assignments
Finish the capstone and present it for review
Meet the attendance and assessment criteria for the batch
Receive your Assure Success course-completion certificate
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.
Imbalanced data
Handle rare outcomes with recipes steps and appropriate metrics.
Model explanation
Explain predictions using variable importance and DALEX.
Your future
Careers after this course
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.
Expert trainers
Certified instructors with real project experience.
Exam practice
Practice questions and mock tests.
Flexible batches
Weekday and weekend batches.
Exam support
Help with eligibility, vouchers and booking.
Ready to get certified?
Course fee ₹42,999. Enroll at assuresuccess.in/course/machine-learning-with-r
📞 +91 93517 82674✉️ info@assuresuccess.in💬 WhatsApp +91 93517 82674
Assure Success. Exam details are set by the certifying body and may change — check the official website before booking. All trademarks belong to their owners.