Put your data science skills into practice by working on machine learning projects using the classic `mtcars` dataset in R. This course provides hands-on experience with end-to-end solutions, from data preprocessing to model evaluation, ensuring you are prepared for real-world tasks.
Adapting Data Preprocessing Routine
Fix the Data Preprocessing Bug
Data Exploration with mtcars
Preprocessing with Mean Calculation
Exploring the mtcars Dataset
Change the Data Split Ratio
Find and Fix the Errors
Fill in the Missing Code
Splitting and Scaling in R
Split and Scale Your Data
Change Logistic Model Prediction
Fix Bug in Logistic Model
Train a Binary Classifier Model
Train a New Logistic Model
Multiclass Logistic Regression in R
Switch to Random Forest Model
Fix the Prediction Code
Making Predictions and Evaluation
Evaluating Model Predictions
Write Code to Do Predictions and Evaluate
Plot Theme Comparison
Fix the Logistic Regression Plot
Add Missing Parts for Visualization
Visualize Logistic Regression Coefficients
Visualize Variable Importance
Modify Cross-Validation Folds
Fix Cross-Validation Code
Cross-Validate with Seven Folds
Cross-Validation From Scratch