Bassam Almogahed
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.
Instructor Details
Courses : 2
Specification: Imbalanced Learning (Unbalanced Data) – The Complete Guide
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21 reviews for Imbalanced Learning (Unbalanced Data) – The Complete Guide
Add a review
| Price | $29.99 |
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| Provider | |
| Duration | 5 hours |
| Year | 2019 |
| Level | Intermediate |
| Language | English |
| Certificate | Yes |
| Quizzes | No |
$29.99
Mike –
It’s not a common topic in udemy yet it’s yet important topic to be addressed in real application. Instructor is responsive when answering question which is major factor I give 5 stars. However, I think the topic has still much room for expansion and improvement, like more practical example for dealing large features with minority class, outlier, data with many categorical with minority class,etc. Perhaps also can provide real life complex example that need multiple step to deal with minority class based on his works as data science
Amgad Abdullah Almogahed –
Very helpful and informative course, especially in conjuction with a machine learning course. Example materials were well organized and provided good case studies. Instructor was extremely professional and pleasant to learn from.
Dustin Yates –
good
Dimitar –
Great specific topic in data mining. I was looking for a course or a website that discusses all these algorithms in details in one place. Thanks a lot!
Pablo C –
very useful and informative. thank you for the course
John Salzer –
great lectures and thanks for providing source code for the examples.
Sharon Black –
Very interesting course! All concepts are detailed and explained with examples and to the point. Bravo
John Colins –
Great list of algorithms and good examples.
Micheal O –
lectures are well prepared and straight to the point. I appreciate that.
Amjad Abdullah –
Instructor covered many algorithms. clear explanations and good examples.
Markus Maresch –
Technically good and profound knowledge. Of the 8 or so algorithms, those only differ in 3 5 lines, and have lots of repetitions this is annoying and could be improved. The supporting material could be improved, as there also many repetitions. The entire supporting material could be provided in ONE zip file. The scatter plot could be done with seaborn directly not only shown as image. Also, the final example is a bit messy with the before/after logic again this could be fixed easily in order to make it clearer. The last two sections are very good.
Chris Silva –
All in all, the course is worthwhile and useful if you have some background in machine learning.
Jo Sung –
I really like the examples after each method explained especially the visual representation. can you add links to the original papers that introduced these methods? thanks
Anda B –
Specialized topic and well explained!
Riza –
Excellent course. Bassam explains the core concepts and go over many algorithms. He is clear and concise and the course is well planned.
Dat W –
Great Course! It covers a specific problem in Machine Learning. Instructor covers causes, consequences and solutions in details.
Michael Sanderson –
The course has great coverage and comes in bite sized pieces.
Cristian Balan –
There is a clear repetitive pattern in the sessions presenting the methods
Utkarsh Mittal –
Till this point no practical example
Tougov Dmitriy –
Very good
Ayon Banerjee –
Loved the course. Looking forward to updates to the course in the future and the requested presentation slides.