ooresAnalytics Training
Step Into the Future — Master Machine Learning, AI & R!
Transform your data skills through real projects, interactive labs, and expert mentorship.
Feb. 07 – Mar. 08 2025
Live via Zoom
Machine Learning in R (10-Day Weekend Program)
Duration: 10 Days (5 weekends)
Schedule: Saturdays & Sundays, 9:00–10:30 AM ET (New York Time)
Registration closes: October 31, 2025
Format: Live via Zoom
Payment Options: Bank Transfer, Venmo, or Zelle accepted.
Course Fee: $250 (limited scholarships and variable pricing available based on Country of participant)
About the Training
This 10-day Machine Learning in R program covers the full lifecycle — from data preparation to deployment. Learn with hands-on labs, real datasets, and mentorship from R experts.
- Supervised & Unsupervised Learning
- Model Tuning & Explainability
- Real-World Capstone Project
Who Should Register
Designed for advanced undergraduates, graduate students, faculty, and applied researchers aiming to apply ML in their work.
- Basic R and statistics familiarity
- No prior ML experience required
- Conceptual, not purely mathematical approach
Learning Objectives
Apply ML workflows in R
Big data & data lakes
Implement feature engineering
Communicate findings via R Markdown or Shiny
Weekly Breakdown
Week 1: Introduction & Data Wrangling in R
- Overview of ML concepts and workflow
- Data types, features, and labels
- Model training vs testing process
- Using real datasets for exploration (e.g., Palmer Penguins)
- Practical examples in R showing how caret and tidymodels integrate
Week 2: Feature Engineering & Preprocessing
- Handling missing values, scaling, and encoding
- Feature normalization using both caret and tidymodels
- Real dataset: Heart Disease
- Assignment: create and compare preprocessing pipelines
Week 3: Linear Models & Regularization
- Linear and multiple regression models
- Regularization (Ridge, Lasso, Elastic Net)
- Evaluation metrics (RMSE, MAE, R²)
Implementation using both caret and tidymodels
Week 4: Logistic Regression & Classification Metrics
- Logistic Regression & KNN classifiers
- Model performance metrics: Accuracy, ROC, AUC, F1
- Implementation with both caret and tidymodels
Visualization with ggplot2
Week 5: Tree-Based Methods
- Decision Trees, Random Forests, and Boosting (XGBoost)
- Bagging vs Boosting theory and implementation
- Performance evaluation and tuning
- Bonus: Feature importance visualization using vip and DALEX
Week 6: Unsupervised Learning
- K-Means and Hierarchical Clustering
- PCA for dimensionality reduction and visualization
- Interpreting clusters and components
- Practice using cluster, factoextra, and ggplot2
Week 7: Model Interpretability & Explainability
- Understanding overfitting and underfitting
- K-fold and repeated cross-validation
- Grid and random search hyperparameter tuning
Building end-to-end ML pipelines with tidymodels and caret
Week 8: Advanced / Specialized Models
- Feature importance and post-hoc model interpretation
- Partial Dependence Plots (PDP) and DALEX explainers
- Local explanations using LIME and SHAP
- Ethical implications and responsible ML transparency
Week 9: Model Evaluation, Validation & Deployment
- Deploying models using Shiny and plumber
- Model serialization with saveRDS() / readRDS()
- Building interactive prediction interfaces
- REST API creation for programmatic model access
- Best practices for versioning and reproducibility
Week 10: Capstone Project & Presentations
- Full end-to-end ML workflow integration
- Real project selection & execution (classification, regression, clustering)
- Evaluation, interpretation, and optional deployment
- Deliverables: R scripts, report, and presentation
- Grading rubric & next steps for professional growth
Each week includes:
🎓 Lecture slides (PowerPoint)
💻 R lab exercises (caret + tidymodels)
📊 Real open datasets
Computing & Tools
All analyses are done in R using packages like caret, tidymodels, ggplot2, and DALEX.
All example files and syntax are downloadable from the course portal.