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Practical Machine Learning with the mtcars Dataset in R

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Overview

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.

Syllabus

Unit 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

Unit 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

Unit 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

Unit 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

Unit 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

Unit 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

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