After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
Machine learning is the study and application of algorithms that learn from and make predictions on data. From search results to self-driving cars, it has manifested itself in all areas of our lives and is one of the most exciting and fast growing fields of research in the world of data science. This course teaches the big ideas in machine learning: how to build and evaluate predictive models, how to tune them for optimal performance, how to preprocess data for better results, and much more. The popular caret R package, which provides a consistent interface to all of R's most powerful machine learning facilities, is used throughout the course.
Prerequisites
Curriculum
Course outline
1
Regression Models: Fitting and Evaluating Their Performance
In the first chapter of this course, you'll fit regression models with
train() and evaluate their out-of-sample performance using cross-validation and root-mean-square error (RMSE).- Welcome to the course50 XP
- In-sample RMSE for linear regression50 XP
- In-sample RMSE for linear regression on diamonds100 XP
- Out-of-sample error measures50 XP
- Out-of-sample RMSE for linear regression50 XP
- Randomly order the data frame100 XP
- Try an 80/20 split100 XP
- Predict on test set100 XP
- Calculate test set RMSE by hand100 XP
- Comparing out-of-sample RMSE to in-sample RMSE50 XP
- Cross-validation50 XP
- Advantage of cross-validation50 XP
- 10-fold cross-validation100 XP
- 5-fold cross-validation100 XP
- 5 x 5-fold cross-validation100 XP
- Making predictions on new data100 XP
2
Classification Models: Fitting and Evaluating Their Performance
In this chapter, you'll fit classification models with
train() and evaluate their out-of-sample performance using cross-validation and area under the curve (AUC).3
Tuning Model Parameters to Improve Performance
In this chapter, you will use the
train() function to tweak model parameters through cross-validation and grid search.4
Preprocessing Data
In this chapter, you will practice using
train() to preprocess data before fitting models, improving your ability to making accurate predictions.5
Selecting Models: A Case Study in Churn Prediction
In the final chapter of this course, you'll learn how to use
resamples() to compare multiple models and select (or ensemble) the best one(s).R
Machine Learning with caret in R
Course
Complete
