Course · Advanced
Introduction to Machine Learning in R
In this course, you'll learn the fundamentals of machine learning using R.
- Advanced friendly
- 1 hrs
- 5 lessons
- 1 project
- Premium
Course overview
Implement core machine learning workflows in R using k-nearest neighbors, error metrics, and cross-validation to build reliable models.
What's inside
5 lessons · 1 project
- 01 25 minIntroduction to Machine Learning Concepts
Learn the basics of machine learning using the k-nearest neighbors algorithm
- 02 19 minEvaluating Model Performance
Learn how to test models using error metrics and train-test validation.
- 03 19 minMultivariate K-Nearest Neighbors
Improve your model predictions by using more features.
- 04 12 minCross Validation
Learn how to use k-fold cross validation to perform more rigorous testing
- 05 9 minHyperparameter Optimization
Vary the k value to further improve performance.
- 06 3 minGuided Project: Predicting Car Prices Project
For this project, we'll step into the role of data scientists to predict car prices using the k-nearest neighbors algorithm and R, practicing the machine learning workflow on a real dataset.
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