- Overview
- Curriculum
- Discussion
- Review
- Instructor
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Understand the underline causes of the Class Imbalance problem
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Why it is a major challenge in machine learning and data mining fields
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Learn the different characteristics of imbalanced datasets
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Learn the state-of-the-art techniques and algorithms
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Understand variety of data based methods such as SMOTE, ADASYN, B-SMOTE and many more!
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Apply Data-Based Techniques in practice
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Understand different algorithmic approaches such as: One Class Learning, Cost Sensitive Learning and more!
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Apply Algorithmic-Based methods in practice
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Learn how to correctly evaluate a prediction model built using imbalanced data
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Learn strategies and recommendations to help you avoid pitfalls when working with imbalanced dataset
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Prior knowledge in machine learning/data science is necessary or at least currently enrolled in a machine learning course.
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This course is for students and professionals who are working in the machine learning / data science area and want to increase their knowledge and skills. It is also for students who are currently taking a course in these areas. It is not for students with no background knowledge in Machine Learning.
This is a niche topic for students interested in data science and machine learning fields. The classical data imbalance problem is recognized as one of the major problems in the field of data mining and machine learning. Imbalanced learning focuses on how an intelligent system can learn when it is provided with unbalanced data.
There is an unprecedented amount of data available. This has caused knowledge discovery to garner attention in recent years. However, many real-world datasets are imbalanced. Learning from unbalanced data poses major challenges and is recognized as needing significant attention.
The problem with unbalanced data is the performance of learning algorithms in the presence of underrepresented data and severely skewed class distributions. Models trained on imbalanced datasets strongly favor the majority class and largely ignore the minority class. Several approaches introduced to date present both data-based and algorithmic solutions.
The specific goals of this course are:
Help the students understand the underline causes of unbalanced data problem.
Go over the major state-of-the-art methods and techniques that you can use to deal with imbalanced learning.
Explain the advantages and drawback of different approaches and methods .
Discuss the major assessment metrics for imbalanced learning to help you correctly evaluate the effectiveness of your solution.
Introduction
Introduction
Problem Definition
Problem Definition
How Common is this problem?
How Common is this problem?
Prerequisites & Course Outcomes
Prerequisites & Course Outcomes
The Four Different Characteristics
The Four Different Characteristics
How Hard is my Unbalanced Dataset?
How Hard is my Unbalanced Dataset?
Datasets - Quick Guide
Datasets - Quick Guide
Languages & Source Code
Languages & Source Code
Installing Anaconda for Mac
Installing Anaconda for Mac
Installing Anaconda for Windows
Installing Anaconda for Windows
Data-based Approaches Introduction
Data-based Approaches Introduction
Undersampling Methods Introduction
Undersampling Methods Introduction
Undersampling: Random Undersampling
Undersampling: Random Undersampling
Example - Random Undersampling
Example - Random Undersampling
Tomek Link
Tomek Link
Practical Example - Tomek Link
Practical Example - Tomek Link
UnderSampling: One Sided Selection
UnderSampling: One Sided Selection
Practical Example - OSS
Practical Example - OSS
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4.7
78 Reviews
Bassam Almogahed
Instructor
This Course Includes
