Machine Learning Engineering with R
Machine Learning Engineering with R provides a practical, project-focused path to mastering machine learning methods, data preparation, and model deployment. Designed for software developers, statisticians, and data analysts, this course blends solid theory with real-world practice, enabling you to build, assess, and deliver production-ready machine learning applications.
Topics Covered in This Course
Supervised Learning
Build predictive models with algorithms such as linear and logistic regression, decision trees, and random forests. Learn how to train, validate, and tune models for tasks like classification and regression using R’s powerful packages such as caret, tidymodels, or mlr3.
Unsupervised Learning
Discover hidden structures in data through clustering methods like K-means and hierarchical clustering, and perform dimensionality reduction with techniques such as PCA and t-SNE to simplify and interpret complex datasets.
R Machine Learning Ecosystem
Develop hands-on skills with R’s core machine learning frameworks including tidymodels, caret, and mlr3. Create reproducible workflows, implement cross-validation, optimize hyperparameters, and streamline feature engineering.
Data Wrangling with dplyr and tidyverse
Master data import, cleaning, transformation, and merging using R’s tidyverse tools such as dplyr, tidyr, and readr. Prepare datasets efficiently for machine learning pipelines.
Numerical Computing with base R and matrix operations
Gain confidence in vectorized operations, broadcasting, and matrix computations. Learn to handle high-performance numerical tasks and manipulate large datasets using base R and supporting libraries.
Model Evaluation and Validation
Evaluate models with metrics such as accuracy, precision, recall, F1 score, ROC-AUC, and mean squared error. Apply techniques like k-fold cross-validation, resampling strategies, and learning curves to ensure robust, generalizable performance.
Model Deployment with R
Learn to save and share models using R’s serialization tools and deploy them as RESTful APIs with plumber. Explore containerization with Docker and understand best practices for running R models in production environments.
Introduction to MLOps
Get an introduction to version control, CI/CD pipelines, automated testing, model monitoring, and retraining. Build the skills to manage and maintain machine learning models after deployment.
Career Accelerator Program
Premium Career-Ready Track
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