R tidymodels part 2: Beyond linear regression

Level Up Your Regression Modeling with Advanced Techniques in R

Ready to move beyond basic linear regression and regularization? This course is designed for data scientists and analysts looking to master advanced regression techniques in R using the powerful tidymodels framework. Build robust predictive models for real-world challenges with confidence.

What You'll Learn:

• Non-Linear Regression: Explore flexible, non-parametric regression using k-nearest neighbors (KNN).
• Decision Trees: Develop interpretable, rule-based models with decision trees.
• Ensemble Modeling: Apply random forests for building robust and accurate predictive models.
• Boosting Algorithms: Harness the power of XGBoost and LightGBM, two of the fastest and most effective tree-based learners.
• Bagging and Boosting Principles: Understand the underlying principles of bagging and boosting techniques.
• Model Tuning: Efficiently tune hyperparameters using grid search and Bayesian optimization.
• Model Evaluation: Compare model performance using consistent metrics across different algorithms.
• Scalable Modeling Workflows: Structure your modeling workflow for improved scalability, readability, and reproducibility.

Why Take This Course?

Modern data science demands more than simple models. This course equips you with the skills to adapt to complex, non-linear relationships in real-world data. Learn to select the right model and justify your choices, moving beyond syntax to truly understand machine learning principles. This course emphasizes the tidyverse and tidymodels philosophy for elegant and consistent modeling.

What You’ll Get:

• Clear explanations of advanced modeling concepts.
• Intuitive insights into ensembles, bagging, and boosting.
• Step-by-step implementations of each algorithm in R.
• Practical coding examples using real datasets.
• Exercises and assignments to reinforce your learning.
• Solutions for all exercises and assignments.
• A final capstone modeling project to apply your skills.
• All code, datasets, and solutions are provided.
• Lifetime access to course materials.

Who Is This Course For?

• Students with a basic understanding of the tidyverse and tidymodels. (Prior knowledge of course part 1 is strongly recommended.)
• Data analysts and data scientists seeking to master regression with modern algorithms.
• R users looking to expand beyond linear models into flexible, high-performing solutions.
• Anyone interested in ensemble methods, model tuning, and boosting strategies in R.

If you're ready to enhance your R modeling skills and learn the most powerful regression tools within the tidymodels framework, this course is perfect for you. Enroll today and take your predictive modeling to the next level!