This is the bite size course to learn R Programming for Machine Learning and Statistical Learning. In CRISP DM data mining process, machine learning is at the modeling and evaluation stage. 

You will need to know some R programming, and you can learn R programming from my "Create Your Calculator: Learn R Programming Basics Fast" 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, knn, neural network, linear regression, and evaluate your models very soon after learning the course.

You can take the course as follows, and you can take a exam at EMHAcademy to get SVBook Certified Data Miner using R certificate : 

- Create Your Calculator: Learn R Programming Basics Fast (R Basics)

- Applied Statistics using R with Data Processing (Data Understanding and Data Preparation)

- Advanced Data Visualizations using R with Data Processing (Data Understanding and Data Preparation, in future)

- Machine Learning with R (Modeling and Evaluation)

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