The Post Java Machine Learning Weka Intermediate for 7 hours
Udemy Coupon ED | The Post Java Machine Learning Weka Intermediate for 7 hours2nd class for everyone who want to widespread java machine learning. Introduce Weka, which can both design and program Java machine learning. 2. What (Lectures). Here's a real machine learning application Get Course
What you'll learn
- Practical Application of Java Machine Learning Using Weka
- Adopting an Optimal Algorithm by Comparative Analysis
- Drawing up the basis for decision making using feature (attribute) selection only
- Formal text mining such as surveys
- Proof of relationship by classification analysis after association analysis
- Application of Artificial Neural Network for Image Analysis
- Weka and R Program Interlink
The goal is to quickly establish a decision cooperative system with data.:
Introduce Weka, which can both design and program Java machine learning.
2. What (Lectures)
Here's a real machine learning application with weka alone
: we've adapted a variety of application cases into familiar content.
Then shall we briefly introduce the contents?
2.1 Adopting an Optimal Algorithm by the Experimenter
: Adopting an Optimal Model through a P-value Statistical Test (where did you hear that?)
2.2 Making decisions with the feature selection
: creating decision information with a specific (attribute) selection. R program linkage is a bonus.
2.3 Survey Text Mining
: No more wrestling with the difficult Hangul Formosa! A simple Korean survey can be done with basic functions.
2.4 Correlation and Classification of U.S. House Elections in 84
: The Obama camp did not anticipate election pledges, but chose statistical analysis of its website to raise campaign funds: What's really important is to know which pledges are directly linked to election?
2.5 Image Analysis by Artificial Neural Network and Image Filter
: Tired of waiting for a beta version of dl4j: Introducing Weka's built-in Artificial Neural Network and wekadeepleasing4j.
2.6 Estimate when a course is completed through regression analysis
: Used to determine how long a lecture should be postponed.
The above process is explained in three order as follows.
3.1 Theoretical Description
: Background knowledge is brief. It's really simple, it's all about the point.
3.2 KnowledgeFlow Design
: Weka's Best Advantage - Machine learning is possible without programming.
3.3 Java programming
: Another advantage of weka, weka provides everything for design and coding.
4. IF (effectiveness)
You can apply loaded data analysis to traditional IT systems that consist of Java platforms
: Would you like to know how to take a post way in traditional IT so that you can analyze it well on ICBMs?
You get a way to understand the real world with your data
: to understand the unseen reality with your data.
5. Beginner's reflection point → Supplementary intermediate course
It's an intermediate course that has improved more than a beginner's class for the base change of Java machine learning.:
Through feedback and self-reflection from the students on the beginner's course, we have improved more than the beginner's course.
6. Lecture environment
Use weka 3.9.3 for Windows OS
(3.9.4 has a bug when importing ANSI type files).
7. Lecture materials
: You can download it by clicking the cloud icon in the second class in section 1
(installing Weka software and downloading course materials). (55 MB)
8. Continuation of Extra lecture
I will continue to upload lecture from the students' good questions and useful information from other media.
I look forward to strengthening communication with my students and improving their satisfaction with lectures.
Exclude hard, requirements, or personal questions.
※ You can see Korean in the video or lecture materials.
※ But you don't have to worry that you don't understand Korean.
※ The Korean language you see in the lecture can be considered to only non-utf-8 data.