Practical Machine Learning Course Syllabus

This course provides a hands-on introduction to practical machine learning using R’s caret package. Learners will follow the complete predictive modeling workflow—from study design and data splitting to model training, evaluation, and combination. With approximately 9 hours of content and a final project, this course is ideal for those seeking real-world experience in building and validating models on real datasets. Lifetime access allows flexible, self-paced learning.

Module 1: Introduction to Prediction and Study Design

Estimated time: 2 hours

• Overview of predictive modeling concepts
• Understanding training and test sets
• Types of error in prediction
• Designing a machine learning study with cross-validation

Module 2: Machine Learning with caret in R

Estimated time: 2 hours

• Using the caret package for model training and evaluation
• Data splitting and preprocessing techniques
• Scaling predictors and applying PCA
• Plotting and interpreting model predictors

Module 3: Decision Trees, Random Forests, and Boosting

Estimated time: 1.5 hours

• Building and interpreting decision trees
• Implementing random forests for improved accuracy
• Introduction to boosting and ensemble tree methods

Module 4: Regularization and Model Combination

Estimated time: 2 hours

• Understanding regularized regression (ridge and lasso)
• Combining multiple models to improve predictions
• Introduction to forecasting and unsupervised prediction methods

Module 5: Final Project

Estimated time: 2 hours

• Build a working prediction model using real data
• Apply preprocessing, model training, and validation techniques
• Submit model for peer review to earn certificate

Prerequisites

• Familiarity with R programming
• Basic knowledge of statistics and data analysis
• Experience with data manipulation and visualization in R

What You'll Be Able to Do After

• Design and implement a complete predictive modeling workflow
• Use the caret package to build, tune, and evaluate machine learning models
• Apply tree-based and regularized models to real datasets
• Combine models to enhance prediction accuracy
• Interpret and communicate model performance effectively