Unbalanced Data – Quick Start by Bassam Almogahed

Udemy course Unbalanced Data – Quick Start by Bassam Almogahed

Unbalanced Data – Quick Start is the best Udemy course on the market. With this offer they will be able to greatly improve their knowledge and become more competitive within the Development category. Therefore, if you are looking to improve your Development skills we recommend that you download Unbalanced Data – Quick Start udemy course.

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Course data:

  • Author: Bassam Almogahed
  • Course rating: 4.2
  • Category: Development
  • Modality: Online
  • Status: Available
  • Idiom: English

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Abouth Bassam Almogahed

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.

Unbalanced Data - Quick Start

What the udemy Unbalanced Data – Quick Start course teaches?

What you’ll 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 couple data-based undersampling techniques and apply them. Understand couple data-based oversampling techniques and apply them Learn an algorithmic-based algorithm

Learn what is imbalanced learning is all about: causes, consequences and main solutions to handle unbalanced datasets

More information about the course Unbalanced Data – Quick Start

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 imbalanced data poses major challenges and is recognized as needing significant attention. The problem with imbalanced 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 this problem Discuss the different characteristics of an unbalanced dataset Highlight the severity and importance  of this branch of data science Give a general idea of the two main major state-of-the-art approaches that you developed to handle this problem. Go over two methods in details to give an idea about some of the techniques used and hopefully motivate the students to learn more.

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