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Deep Learning & Machine Learning

Java Machine Learning Weka Intermediate

This is the second lecture for popularizing Java machine learning. We introduce Weka, which provides UI and API so that both design and coding can be implemented. We have included cases that are completely suitable for practical application in the lecture.

(4.8) 6 reviews

54 learners

  • javaraml
Java
Machine Learning(ML)
Weka

Reviews from Early Learners

What you will learn!

  • How to apply Java machine learning in practice using weka

  • Adoption of optimal algorithm through comparative analysis

  • Decision-making basis derivation using only feature selection

  • Structured text mining such as Hangul survey

  • Proof of causal relationship through classification analysis after association analysis

  • Application of artificial neural networks for image analysis

  • Integration of weka and R programs

Want to implement machine learning in Java?
👉 Try using machine learning in your work with the intermediate level of Java Machine Learning Weka .


1. why (purpose)

The goal is to build a collaborative system that enables rapid decision-making using data.
: Introducing weka, which enables both design and programming of Java machine learning.

2. what (lecture content)

This is a practical machine learning application case implemented solely with Weka.
: We've adapted various application cases into familiar content. So, let's briefly introduce the content.

2.1 Adoption of the optimal algorithm by the experimenter
: Selecting the optimal model through statistical testing of significance level (p-value) (Have you heard this before?)

2.2 Presenting the basis for decision-making based on feature selection alone
: You can create decision-making information simply by selecting specific attributes. Integration with R programs is a bonus.

2.3 Korean Survey Text Mining
: No more struggling with difficult Korean morphemes! Simple Korean surveys are possible with just the basic features.

2.4 Correlation and classification analysis of the 1984 U.S. House of Representatives election results
: The Obama camp did not predict the election pledges, but rather selected the website to raise campaign funds through statistical analysis.
: What's really important is knowing which promises are directly linked to getting elected, right?

2.5 Image Analysis Using Artificial Neural Networks and Image Filters
: I've been waiting for a long time for dl4j to be in beta.
: Introducing the built-in artificial neural network provided by Weka and wekadeeplearning4j.

2.6 Estimating course completion time through regression analysis
: I used it to figure out how long I should delay the release of the lecture.

3. Method

The above process is explained in the following three steps.

3.1 Theoretical Explanation
: I'll give you a brief background explanation. Really simply, just the essentials.

3.2 KnowledgeFlow Design
: Weka's biggest advantage is that you can do machine learning without knowing programming.

3.3 Java Programming
: Another advantage of Weka is that Weka provides everything for design and coding.

4. IF (GET)

You can apply data analysis loaded on traditional IT systems built on the Java platform.
: A journey of a thousand miles begins with a single step. If you know how to analyze traditional IT, wouldn't you be able to analyze ICBMs well?

You will learn how to understand the real world with data.
: The goal is to understand the previously invisible reality through data.

5. Beginner course reflection → Intermediate course supplementation

This is an intermediate course, improved from the beginner level, to popularize Java machine learning.
: I have improved the beginner course through feedback from students and self-reflection.

6. Lecture environment

I use weka 3.9.3 on Windows OS.
(3.9.4 has a bug when loading ANSI type files.)

7. Lecture materials

After registering for the course, in the second lesson of Section 1 (installing Weka software and downloading lecture materials)
Click the cloud icon to download (55MB)

8. Survey

We are conducting a five-item survey to help establish the direction of our advanced Java machine learning course. We ask for your participation.
http://bit.ly/javamachinelearning_survey

9. Continuation of the extra lecture

We will continue to upload ideas gleaned from students' valuable questions and useful information from other media as supplementary lectures.

I look forward to strengthening communication with my students and improving their satisfaction with the lectures.

Solid, excluding requests or personal questions.

View previous lectures

Java Machine Learning Weka Beginner
Machine Learning with Java: An Introduction to Weka

Recommended for
these people

Who is this course right for?

  • Those who want to apply weka in practice

  • Anyone who wants to learn how to use Weka Experimenter and KnowledgeFlow

  • For those who want additional evidence for the association analysis (shopping cart) analysis

  • Those who need to apply text mining and image analysis in practice using Java

Need to know before starting?

  • Complete the Java Machine Learning Weka Beginner (Free) Course

  • ADsP (better to know)

  • Java (better to know)

Hello
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Answers

4.9

Rating

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Courses

정보화 기획/구축/진단 업무를 수행하였고 스몰데이터분석을 실무에 적용하고 있습니다.
현재 데이터분석 분야는 코딩이 과대포장된 진입장벽을 만들었다는 것을 알게 되었습니다.
이제는 거품을 걷어내고 데이터분석의 저변화와 자바머신러닝을 준비하고
직접 강좌로 자바머신러닝을 확산할 동료들을 만나는 것이 저의 목적입니다.
더나아가 POST 정보화시대를 대비하고 영위하는 미래의 모습을 그려봅니다.

Curriculum

All

30 lectures ∙ (6hr 14min)

Course Materials:

Lecture resources
Published: 
Last updated: 

Reviews

All

6 reviews

4.8

6 reviews

  • dsaqaz4217009님의 프로필 이미지
    dsaqaz4217009

    Reviews 9

    Average Rating 5.0

    5

    100% enrolled

    Overall, it's good content. Java machine learning is an unfamiliar field, but there are almost no lectures introducing it, so it was very helpful. However, since I mostly use Java for web, it's a bit difficult.

    • javaraml
      Instructor

      Just as Java played a pivotal role in transitioning from web (jsp) to mobile (android), it is expected that Java will play a major role in data analysis (weka). Thank you for your good review.

  • jiu4163님의 프로필 이미지
    jiu4163

    Reviews 10

    Average Rating 5.0

    5

    100% enrolled

    It has been a great help in my overall project on machine learning. Thank you!

    • twotone3654382님의 프로필 이미지
      twotone3654382

      Reviews 24

      Average Rating 4.5

      4

      100% enrolled

      It was a very informative lecture.

      • rvlab님의 프로필 이미지
        rvlab

        Reviews 4

        Average Rating 5.0

        5

        100% enrolled

        This is a really good lecture. It's easy and good, so I don't get bored watching it.

        • javaraml
          Instructor

          Thank you for taking this course with interest, even though it may have been a tough subject. I wish you success.

      • nyj93081211님의 프로필 이미지
        nyj93081211

        Reviews 1

        Average Rating 5.0

        5

        23% enrolled

        I was very satisfied with the feedback on quick questions and the supplementary content for the missing content. The lecture content was also explained well so that even beginners could easily understand it, which was very helpful. Thank you.

        • javaraml
          Instructor

          Hello. Thanks to you, I learned a new way to communicate with students. Javara Machine Learning will continue to work hard in the future.

      $26.40

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