Practical Machine Learning with the mtcars Dataset in R
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.
Lessons and practices
Lesson 1: Preprocessing and Exploring the mtcars Dataset
Adapting Data Preprocessing Routine
Fix the Data Preprocessing Bug
Data Exploration with mtcars
Preprocessing with Mean Calculation
Exploring the mtcars Dataset
Lesson 2: Splitting the Data and Feature Scaling
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
Lesson 3: Building and Evaluating a Model
Change Logistic Model Prediction
Fix Bug in Logistic Model
Train a Binary Classifier Model
Train a New Logistic Model
Multiclass Logistic Regression in R
Lesson 4: Making Predictions and Evaluating Performance
Switch to Random Forest Model
Fix the Prediction Code
Making Predictions and Evaluation
Evaluating Model Predictions
Write Code to Do Predictions and Evaluate
Lesson 5: Visualizing Model Results and Feature Importance
Plot Theme Comparison
Fix the Logistic Regression Plot
Add Missing Parts for Visualization
Visualize Logistic Regression Coefficients
Visualize Variable Importance
Lesson 6: Generalizing and Validating Models with Cross-Validation
Modify Cross-Validation Folds
Fix Cross-Validation Code
Cross-Validate with Seven Folds
Cross-Validation From Scratch
Interested in this course? Learn and practice with Cosmo!
Practice is how you turn knowledge into actual skills.