R tidymodels part 3: Classification

R, Data Science, tidymodels, Machine Learning, Classification, Logistic Regression, KNN, Naive Bayes, Metrics, RStudio

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Description

You’ve mastered regression modeling and explored advanced algorithms; now it’s time to step into the world of classification.

This course is designed for learners who want to build models that predict categories, not numbers, and who wish to understand the statistical and machine learning foundations behind them.

What You’ll Learn

In this course, you’ll move from probability intuition to full-scale classification workflows using tidymodels, R’s modern ecosystem for machine learning:

Understand what classification is and how it differs from regression

Learn the logic of logistic regression and its link to probabilities, odds, and log-odds

Fit and interpret logistic regression models using maximum likelihood estimation

Evaluate models with accuracy, precision, recall, specificity, and F1 score

Visualize model performance through ROC and AUC curves

Adjust probability thresholds and see their effect on predictions

Handle imbalanced data using balanced accuracy, threshold tuning, and advanced metrics

Apply resampling techniques such as upsampling, downsampling, and SMOTE

Build and tune K-nearest neighbors (KNN) classifiers

Explore Naive Bayes as a probabilistic classifier for both numeric and text data

Preprocess text using textrecipes and create a simple spam-filtering model

Compare multiple classification models within the same tidymodels workflow

Why Take This Course?

Classification problems are everywhere — from medical diagnostics and fraud detection to email filtering and customer segmentation.

This course helps you understand how these models make decisions and how to evaluate them responsibly.

You’ll gain not only the technical skills to build classification models but also the intuition to select the right metric and interpret model behavior — all while keeping your work tidy, reproducible, and explainable.

What You’ll Get

Clear, structured explanations of classification theory and practice

Step-by-step modeling workflows in R and tidymodels

Real-world examples and visual explanations of metrics

Exercises and assignments with full solutions

All code, datasets, and outputs provided

Lifetime access and updates

Who this course is for:

• Anyone interested in data science or machine learning
• Learners who want to understand classification modeling in R
• Anyone who wants to analyze and predict categorical outcomes
• Data analysts and scientists who want to extend their tidymodels skills
• Anyone curious about logistic regression, KNN, or Naive Bayes algorithms
• Those who wish to learn how to evaluate models using real-world metrics
• Professionals who work with imbalanced data or probability-based predictions
• Students and researchers building classification models
• R users aiming to deepen their modeling expertise within the tidyverse/tidymodels ecosystem
• Data scientists who mainly use Python and want to extend their skills into R