التنقيب عن البيانات | eLearning Centre - IUG - Video Lectures

eLearning Centre - IUG - Video Lectures

eLearning Centre - IUG - Video Lectures

قناة المحاضرات المصورة . الجامعة الإسلامية بغزة - فلسطين .

تفاصيل الكورس

دروس الكورس

  1. 1 | Lecture 1: Introduction to Data Mining 00:56:23
  2. 2 | Lecture 2: Data Mining 01:08:05
  3. 3 | Data Mining Lecture 3: Introduction to Data Mining III 01:17:07
  4. 4 | Data Mining Lecture 4: Data Understanding and Preparation 01:08:43
  5. 5 | Data Mining Lecture 5: Data Understanding and Preparation - 5 01:02:40
  6. 6 | Data Mining Lecture 6: Data Understanding and Preparation - 6 00:54:23
  7. 7 | Data Mining Lecture 7: Data Understanding and Preparation - 7 01:10:55
  8. 8 | Data Mining Lecture 8: Data Understanding and Preparation - 8 01:14:02
  9. 9 | Data Mining Lecture 8 : Ch2 Data Preparation example 00:43:30
  10. 10 | Data Mining Lecture 9: Classification -1 01:05:15
  11. 11 | Data Mining Lecture 10 : Classification- 2 01:13:37
  12. 12 | Data Mining Lecture 11: Classification- 3 01:11:07
  13. 13 | Datamining Lecture 12: Classification- 4 00:54:28
  14. 14 | Datamining Lecture 13: Classification-5 00:27:50
  15. 15 | Data Mining 03 - Classificastion-06 00:42:16
  16. 16 | Data Mining 03 - Classification Lab Coding - using Kaggle Notebook 00:50:40
  17. 17 | Data Mining 04 - Regression - 01 00:32:43
  18. 18 | Data Mining 04 - Regression - 02 00:20:11
  19. 19 | Data Mining 05 - Clustering - Part1 00:23:25
  20. 20 | Data Mining 05 - Clustering - Part2 00:42:29
  21. 21 | Data Mining 05 - Clustering - Part3 00:35:40
  22. 22 | Data Mining 05 - Clustering - Part4 00:16:40
  23. 23 | Data Mining 05 - Clustering - Python Coding 00:40:38
  24. 24 | Data Mining 06 - Association Rules - Part1 00:34:51
  25. 25 | Data Mining 06 - Association Rules - Part2 00:36:18
  26. 26 | Data Mining 06 - Association Rules - Part3 00:35:44
  27. 27 | Data Mining 06 - Association Rules - Part4 00:22:38
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    و
    ولاء

    21-04-2024
    ر
    رغد العنزي

    29-02-2024
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    سمو العنزي

    24-02-2024
    D
    Doja AbdulMuslih

    15-02-2024
    M
    Muneerh Jayez

    07-02-2024
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    روان ناير

    06-02-2024
    A
    Ahmed Haroun

    25-03-2023
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    Hafida

    30-12-2022
    L
    Latifa fayez

    17-02-2024
    كلية تكنولوجيا المعلومات تنقيب البيانات Data Mining

    Faculty of Information Technology – Islamic University Gaza

    Data Mining
    SDEV 3304
    Course Syllabus

    General Information
    • Semester: 2
    Semester 2020.
    • Department: Department of Software Engineering.
    • Instructor: Dr. Iyad Husni Alshami,
    • phone: 00970 8 2860700 Ext:2960
    • email: [email protected]
    • office hours: Saturday – Wednesday 11:00 – 13:00
    • office location: I305
    • Credits: 3Hrs.
    • Meeting time and locations:
    • 201: ST 8:00 – 9:30, I101
    • 101: ST 9:30 – 11:00, I116
    Course’s Description
    This course has been designed to give students an introduction to data mining and hands on experience
    with all phases of the data mining process using real data and modern tools. It covers many topics such as data
    formats, and cleaning; make prediction using supervised and unsupervised learning using Python and other tools,
    and sound evaluation methods; and data/knowledge visualization.
    Course’s Objectives
    This course is designed to achieve a number of goals for each student such as:
    • Providing the fundamental understanding of data mining in order to extract hidden knowledge.
    • Exploring the different data mining tasks to extract knowledge:
    o Classification,
    o Clustering,
    o Association Rules extraction, and
    o Outlier detection.
    • Practicing the data mining project phases
    • Presenting the data in the early stage of data mining projects as well as the extracted knowledge.
    • Provide the students the latest hot topics in data mining field.
    • Strengthen the team work
    Course’s Outcome
    By the end of this course the students should be able to:
    • Identify the meaning of data mining, describe the suitable data for data mining projects, list/identify at
    least five different data mining tasks and evaluate the extracted knowledge for each task.
    • Collect and prepare data set suitably for data mining projects.
    • Use machine learning techniques to perform the different data mining tasks.
    • Analysis and build data mining projects individually or as a team member/leader as well .
    • Adopt the ethics of profession with the sensitive personal data

    Text book & References
    • Text Book: “Data Mining: Concepts and Techniques”, 2
    edition by Jiawei Han and Micheline
    Kamber, Morgan Kaufmann 2006.

    • Additional Books:
    • “Data Mining – Practical Machine Learning Tools and Techniques”, 2
    edition by Ian H. Witten
    and Eibe Frank, Elsevier 2005.

    Course’s Outline “topics that will be covered”

    Teaching methods
    • Lectures,
    • Discussion groups,
    • Team work,
    • Using Videos and Presentations
    Evaluation criteria “Grades”
    • 10% Quizzes & Assignments,
    • 10% Participating in Course’s Activities
    • 20% Midterm Exam
    • 20% Final Project
    • 40% Final Exam.
    Course’s Tools
    • PyCharm – Python 3.6
    • Rapidminer Studio

    Course’s Rules
    • The course contents and grading can be changed as necessary.
    • Missing more than 25% of lectures will provide you “W”.
    • There is no predetermined schedule for quizzes.
    • No excuses for missing the quizzes or the assignments.

    يمكنكم متابعة كافة المحاضرات المصورة عبر
    http://lectures.iugaza.edu.ps
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