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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 gain after the course

  • 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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Learners

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Reviews

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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 10

    Average Rating 5.0

    5

    100% enrolled

    Nhìn chung, nội dung tốt. Mặc dù học máy Java là một lĩnh vực còn xa lạ nhưng hầu như chưa có bài giảng nào giới thiệu về nó nên rất hữu ích. Tuy nhiên, nó hơi khó khăn vì nó chủ yếu chạy trên web bằng Java.

    • javaraml
      Instructor

      Giống như Java đóng vai trò quan trọng trong quá trình chuyển đổi từ web (jsp) sang thiết bị di động (Android), Java được kỳ vọng cũng sẽ đóng một vai trò lớn trong phân tích dữ liệu (weka). Cảm ơn bạn đã đánh giá tốt.

  • jiu4163님의 프로필 이미지
    jiu4163

    Reviews 10

    Average Rating 5.0

    5

    100% enrolled

    Nó rất hữu ích cho dự án tổng thể về học máy. Cảm ơn!

    • twotone3654382님의 프로필 이미지
      twotone3654382

      Reviews 24

      Average Rating 4.5

      4

      100% enrolled

      Đó là một bài giảng rất hữu ích.

      • rvlab님의 프로필 이미지
        rvlab

        Reviews 4

        Average Rating 5.0

        5

        100% enrolled

        Đó là một bài giảng rất hay. Đó là một bài giảng dễ dàng và hay, nhưng nó không nhàm chán.

        • javaraml
          Instructor

          Cảm ơn bạn đã quan tâm tham gia khóa học này mặc dù nội dung có thể còn cứng nhắc. Tôi chúc các học viên may mắn.

      • nyj93081211님의 프로필 이미지
        nyj93081211

        Reviews 1

        Average Rating 5.0

        5

        23% enrolled

        Tôi rất hài lòng với phản hồi về các câu hỏi nhanh và nội dung bổ sung cho nội dung chưa đầy đủ. Nội dung bài giảng rất hữu ích vì được giải thích rõ ràng để người mới bắt đầu có thể dễ dàng hiểu được. Cảm ơn bạn ha-ha.

        • javaraml
          Instructor

          Xin chào. Nhờ bạn, tôi đã học được một cách mới để giao tiếp với sinh viên. Chúng tôi sẽ tiếp tục làm việc chăm chỉ để phát triển máy học trong Java.

      $26.40

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