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Imbalanced Learning (Unbalanced Data) – The Complete Guide

Learn how to handle imbalanced data in Machine Learning. Data based approaches, algorithmic approaches and more!
(69 reviews)
660 students
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Platform: Udemy
Video: 4h 47m
Language: English
Next start: On Demand

Table of contents


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.

You will learn

✓ Understand the underline causes of the Class Imbalance problem
✓ Why it is a major challenge in machine learning and data mining fields
✓ Learn the different characteristics of imbalanced datasets
✓ Learn the state-of-the-art techniques and algorithms
✓ Understand variety of data based methods such as SMOTE, ADASYN, B-SMOTE and many more!
✓ Apply Data-Based Techniques in practice
✓ Understand different algorithmic approaches such as: One Class Learning, Cost Sensitive Learning and more!
✓ Apply Algorithmic-Based methods in practice
✓ Learn how to correctly evaluate a prediction model built using imbalanced data
✓ Learn strategies and recommendations to help you avoid pitfalls when working with imbalanced dataset


• Prior knowledge in machine learning/data science is necessary or at least currently enrolled in a machine learning course.

This course is for

• 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.
Machine Learning Specialist
Hello and thank you for checking out my course. I have a B.Sc, M.Sc and PhD in computer science from University of California, San Diego and University of Houston respectively.  
I’m an experienced machine learning specialist. I enjoy working on various aspects of machine learning problems, high-dimensional statistics and predictive analytics with a main focus on developing and analyzing learning algorithms for imbalanced data. I am especially interested in understanding and exploiting the intrinsic structure in data (e.g. manifold or sparse structure) to design more effective learning algorithms. I am an entrepreneur who wants to use technology to improve people’s lives and an educator who wants to turn technology consumers into technology builders. My Method: The first step is always simply noticing a problem that already exists. What could be changed or improved about the way we currently do things to make them easier, cheaper, more efficient or helpful? Next begins the ongoing process of gathering insight. What do people closest to the issue see as the hurdles? How can we collaborate to understand the problem in its most basic form? Third, I map out a clear path from what we have now to a better solution. Finally, I work relentlessly, tirelessly, to come up with an answer while being flexible enough to take criticism and firm enough to stay driven.
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Platform: Udemy
Video: 4h 47m
Language: English
Next start: On Demand

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