R tidymodels part 2: Beyond linear regression

R, Data Science, tidymodels, Machine Learning, Statistics, Regression, Predictive Modeling, XGBoost, LightGBM, RStudio

What you'll learn
- How to develop prediction models using tidymodels framework
- How hyperparameters are being tuned using tidymodels framework
- What is the essence of KNN algorithm
- How to use KNN algorithm for modeling in tidymodels
- How decision trees are built
- How to use decision trees for regression modeling
- How to optimize a decision tree
- What is ensemble learning and ensemble models
- What is bagging
- What is random forest algorithm
- How to use random forest algorithm in tidymodels
- What is the basic idea of parallel computing
- How parallel computing is utilized in ML workflow inside tidymodels framework
- What is boosting
- How boosting is used to develop an extreme gradient boosting (XGBoost) model
- How to use XGBoost and lightGBM models in tidymodels
- How to use high-performance regression models for tabular data

Requirements
- R and RStudio already installed on your computer
- Basic knowledge of statistics is a plus.
- Basic to intermediate R knowledge is a plus.
- If you are a complete beginner to programming or R, you will find this course quite challenging.
- Basic understanding of core tidyverse libraries is a big plus.
- Interest in data science, machine learning, statistics and building predictive models.
- Interest in how to write efficient R code.
- Please update R and / or R's libraries if necessary. List of versions ( R and all R's libraries used in the exercises) provided at the end of each section.
- Finishing course part 1 is strongly recommended

Description
You've built yourfirst predictive models. You understandlinear regressionandregularization. Now it’stime to level up.

This course is designed for learners who want to go beyond simple models and tacklenon-linear relationships,ensemble algorithms, andreal-world modeling challengeswith confidence.

What You'll Learn?

In this course, we remain in theregression domainbut expand your modeling toolbox withpowerful new algorithmsandmodeling strategies:

Usek-nearest neighbors(KNN) for flexible,non-parametric regression

Builddecision treesfor interpretable,rule-based models

Applyrandom forestsforrobust ensemble modeling

Harness the power ofXGBoostandLightGBM, two of thefastest and most powerful tree-based learners

Understand theprinciplesbehindbaggingandboosting

Learn howparallel processingspeeds upmodel tuningandresampling

Tune hyperparameters efficientlywith grids and a Bayesian iterative search approach

Compare models using consistent metrics across algorithms

Structure yourmodeling workflowforscalability,readability, andreproducibility

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

Why Take This Course?

Modern data sciencerequiresmore than just one-size-fits-all models.

With real-world data, relationships are rarely linear. This course teaches you how toadapt,choosetheright model, andjustify your choices.

More than just syntax, this course helps you think like amachine learning practitionerwhile staying fully within the elegant, consistent, and tidy philosophy of tidymodels.

What You’ll Get?

Clearexplanationsof advanced modeling concepts

Intuitive explanations of ensembles, bagging, and boosting

Step-by-step implementationsof each algorithm

Practical coding exampleswith real data

Exercisesandassignmentsto reinforce learning

Solutionsfor all exercises and assignments

A final capstone modeling project

All code, datasets, and solutions provided

Lifetime access

Who Is This Course For?

Students who have a basic understanding of tidyverse  and tidymodels already (it is strongly recommended to first complete course part 1)

Data analystsandscientistswho want tomaster regressionwithmodern algorithms

R users, who are ready tomove beyond linear modelsinto flexible, high-performing learners

Anyone curiousabout ensemble methods, model tuning, and boosting strategies in R

If you'reready to boost your R modeling skillsandlearnthemost powerful regression tools available today, all inside thetidymodels framework, then this course is for you.

Enroll today and take your predictive modeling to the next level!!!

Who this course is for:
- 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
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