This is a bite-size course to learn R programming for machine learning and statistical learning. In the CRISP-DM data mining process, machine learning is at the modeling and evaluation stage.

What will you learn in this course?

You will learn R programming for machine learning and you will be able to train your own prediction models with naive Bayes, decision tree, and, neural network, linear regression, and evaluate your models very soon after learning the course.

Content

1. Getting Started
2. Getting Started 2
3. Getting Started 3
4. Data Mining Process
5. Download Data Set
6. Read Data Set
7. Some Explanations
8. Simple Linear Regression
9. Build Linear Regression Models
10. Predict Linear Regression Models
11. KMeans Clustering
12. KMeans Clustering in R
13. Agglomeration Clustering
14. Agglomeration Clustering in R
15. Decision Tree ID3 Algorithm
16. Decision Tree in R: Split Train and Test Set
17. Decision Tree in R: Train Decision Tree
18. Decision Tree in R: Predict Decision Tree
19. KNN Classification
20. Train KNN in R
21. Predict KNN in R
22. Naive Bayes Classification
23. Naive Bayes in R
24. Neural Network Classification
25. Neural Network in R
26. What Algorithm to Use?
27. Model Evaluation
28. Model Evaluation using R for Classification
29. Model Evaluation using R for Regression