This is a bite-size course to learn Java programming for machine learning and statistical learning with the Weka library. 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 Java 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.

Content

1. Introduction
2. Getting Started
3. Getting Started 2
4. Getting Started 3
5. Data Mining Process
6. Data Set
7. Split Training and Testing Dataset
8. Create a Java Application using Netbeans with Weka Jar
9. Simple Linear Regression
10. Linear Regression using Weka and Java
11. Linear Regression using Weka and Java 2
12. Linear Regression using Weka and Java 3
13. KMeans Clustering
14. KMeans Clustering in Weka and Java
15. Agglomeration Clustering
16. Agglomeration Clustering in Weka and Java
17. Decision Tree ID3 Algorithm
18. Decision Tree in Weka and Java
19. KNN Classification
20. KNN in Weka and Java
21. Naive Bayes Classification
22. Naive Bayes in Weka and Java
23. Neural Network Classification
24. Neural Network in Weka and Java
25. What Algorithm to Use?
26. Model Evaluation
27. Model Evaluation in Weka and Java
28. Create a Data Mining Software
29. Create a Data Mining Software 2