Udemy – R tidymodels part 2: Beyond linear regression

This course is designed for learners who want to go beyond simple models and tackle non-linear relationships, ensemble algorithms, and real-world modeling challenges with confidence.

What You’ll Learn?

In this course, we remain in the regression domain but expand your modeling toolbox with powerful new algorithms and modeling strategies:

• Use k-nearest neighbors (KNN) for flexible, non-parametric regression
• Build decision trees for interpretable, rule-based models
• Apply random forests for robust ensemble modeling
• Harness the power of XGBoost and LightGBM, two of the fastest and most powerful tree-based learners
• Understand the principles behind bagging and boosting
• Learn how parallel processing speeds up model tuning and resampling
• Tune hyperparameters efficiently with grids and a Bayesian iterative search approach
• Compare models using consistent metrics across algorithms
• Structure your modeling workflow for scalability, readability, and reproducibility

And to wrap it all up, you’ll complete a final modeling project, where you build a predictive model on new data, applying everything you’ve learned.

• Anyone who is interested in data science
• Anyone who is interested in statistics
• Anyone who is interested in building predictive models using machine learning
• Anyone who is interested in writing efficient R code
• Anyone whose job, research or hobby is related to building predictive models
• Aspiring data scientists, statisticians or machine learning engineers
• Anyone who deals with data modeling and would like to get familiar with modern R approach for modeling
• Students building predictive models
• Data scientist who mainly use python in their work, and would like to extend their skills into R domain