Machine Learning with Java and Weka

Description

Why learn Data Analysis and Data Science?

According to SAS, the five reasons are

1. Gain problem solving skills

The ability to think analytically and approach problems in the right way is a skill that is very useful in the professional world and everyday life.

2. High demand

Data Analysts and Data Scientists are valuable. With a looming skill shortage as more and more businesses and sectors work on data, the value is going to increase.

3. Analytics is everywhere

Data is everywhere. All company has data and need to get insights from the data. Many organizations want to capitalize on data to improve their processes. It's a hugely exciting time to start a career in analytics.

4. It's only becoming more important

With the abundance of data available for all of us today, the opportunity to find and get insights from data for companies to make decisions has never been greater. The value of data analysts will go up, creating even better job opportunities.

5. A range of related skills

The great thing about being an analyst is that the field encompasses many fields such as computer science, business, and maths.  Data analysts and Data Scientists also need to know how to communicate complex information to those without expertise.

The Internet of Things is Data Science + Engineering. By learning data science, you can also go into the Internet of Things and Smart Cities.

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

You will need to know some Java programming, and you can learn Java programming from my "Create Your Calculator: Learn Java Programming Basics Fast" 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, and 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 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

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